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Cardiorespiratory interactions: Noncontact assessment using laser
Doppler vibrometry
ERIK J. SIREVAAG,a
SARA CASACCIA,b,c
EDWARD A. RICHTER,b
JOSEPH A. O’SULLIVAN,b
LORENZO SCALISE,c
AND JOHN W. ROHRBAUGHa
a
Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
b
Preston M. Green Department of Electrical and Systems Engineering, School of Engineering, Washington University in St. Louis, St. Louis, Missouri,
USA
c
Department of Industrial Engineering and Mathematical Science, Universita Politecnica delle Marche, Ancona, Italy
Abstract
The application of a noncontact physiological recording technique, based on the method of laser Doppler vibrometry
(LDV), is described. The effectiveness of the LDV method as a physiological recording modality lies in the ability to
detect very small movements of the skin, associated with internal mechanophysiological activities. The method is
validated for a range of cardiovascular variables, extracted from the contour of the carotid pulse waveform as a
function of phase of the respiration cycle. Data were obtained from 32 young healthy participants, while resting and
breathing spontaneously. Individual beats were assigned to four segments, corresponding with inspiration and
expiration peaks and transitional periods. Measures relating to cardiac and vascular dynamics are shown to agree with
the pattern of effects seen in the substantial body of literature based on human and animal experiments, and with
selected signals recorded simultaneously with conventional sensors. These effects include changes in heart rate,
systolic time intervals, and stroke volume. There was also some evidence for vascular adjustments over the respiration
cycle. The effectiveness of custom algorithmic approaches for extracting the key signal features was confirmed. The
advantages of the LDV method are discussed in terms of the metrological properties and utility in psychophysiological
research. Although used here within a suite of conventional sensors and electrodes, the LDV method can be used on a
stand-alone, noncontact basis, with no requirement for skin preparation, and can be used in harsh environments
including the MR scanner.
Descriptors: Cardiovascular, Respiration, Other, Autonomic
The goal of this report is to describe in detail an application of a
noncontact method for measuring mechanophysiological activity
based on the technique of laser Doppler vibrometry (LDV). We
include a brief discussion of the underlying theoretical and applica-
tion principles, including methods for LDV signal acquisition and
analysis. We present findings that testify to the effectiveness of the
LDV method for the study of cardiorespiratory signals, validated
within a broad context of convergent information derived from
conventional sensors that are commonly used in psychophysiologi-
cal research. The focus in this report is on changes in cardiovascu-
lar (CV) activity over the respiratory cycle, an issue of abiding
importance for psychophysiologists. Spontaneous breathing, while
resting, provides a procedurally simple and well-characterized
assessment opportunity, although this apparent simplicity belies a
broad and richly textured pattern of effects. Our presentation
emphasizes the breadth of these effects over the respiratory cycle,
using multiple LDV as well as conventional measures, and illus-
trates the capabilities of the LDV as a method for obtaining
advanced measures—on a completely noncontact and unobtrusive
basis.
LDV as a Physiological Recording Method
LDV is widely used in engineering and industrial settings for non-
destructive testing of mechanical vibration (Castellini, Martarelli,
 Tomasini, 2006). LDV is based on the detection of the Doppler
shift in scattered light (in comparison to an internal reference), as
the laser beam is directed at a moving surface. Commercial off-the-
shelf instruments are available in a number of configurations, uti-
lizing several different laser wavelengths (including infrared), scan-
ning capabilities, and optics that support applications ranging from
microscopy to long-range recordings at distances of at least several
hundred meters. A noteworthy feature of the method is the metro-
logical quality, which supports detection of displacements in the
pm range, with a band-pass extending from DC up to vibrations in
the 20–50 kHz range (and well beyond in some systems). Since the
recording basis is noncontact, some of the common problems asso-
ciated with other sensor types (e.g., mass loading, use of adhesives,
intrinsic resonance, cable artifacts) are avoided. As discussed
Address correspondence to: John W. Rohrbaugh, Department of Psy-
chiatry, Washington University School of Medicine, 660 South Euclid
Avenue, St. Louis, MO 63110, USA. E-mail: jwrohrba@wustl.edu
1
Psychophysiology, 00 (2016), 00–00. Wiley Periodicals, Inc. Printed in the USA.
Copyright VC 2016 Society for Psychophysiological Research
DOI: 10.1111/psyp.12638
below, the noncontact basis is of special salience for psychophysio-
logical applications.
The usefulness of LDV as a physiological recording method
lies in the observation that most physiological activities (particu-
larly at the system level) include mechanical energy that is trans-
mitted to the skin. It follows that activities in a range of
physiological response systems can be characterized in terms of
their mechanical representation, especially in the CV system (see
Discussion). Conventionally, mechanocardiovascular activity is
detected by palpation or auscultation, where it forms a critical ele-
ment in the clinical exam (Perloff, 2000). There have been many
methods described over the years for sensing and objectively quan-
tifying such activity, utilizing a range of pressure, microphone,
strain, accelerometry, displacement, proximity, ultrasound, and
other sensors (e.g., Eddleman, 1974; Forrester, Vas, Diamond, 
Silverberg, 1978; Starr, Rawson, Schroeder,  Joseph, 1939;
Tavel, 1972). Specific considerations apply to all of these methods,
with respect to factors such as accuracy and susceptibility to noise,
and especially patient constraint and comfort—which in some
cases would appear to limit their utility for psychophysiological
studies of behaviorally active individuals. Despite such measure-
ment issues, these studies have left a richly detailed literature
regarding the properties of the mechanophysiological activities,
which can guide the interpretation of the LDV signals.
LDV Recording of the Carotid Pulse
As described in voluminous literatures, the mechanophysiological
signs of CV activity are widely distributed over the body, reflecting
variously multivectorial ballistic activity, movements of the precor-
dial surface including heart sounds, and more focal activities over
superficial blood vessels. A substantial scientific and clinical litera-
ture has dwelled with the external carotid pulse (Nichols,
O’Rourke,  Vlachopoulos, 2011; Tavel, 1972), which is the prin-
cipal focus of the research described here. The pulse appears to
arise from changes in lumen diameter associated with variations in
the internal blood pressure (BP). Since the carotid is a distensible
vessel, the diameter is closely related to internal pressure over the
physiological range (Sugawara, Niki, Furuhata, Ohnishi,  Suzuki,
2000)—albeit with some nonlinearity deriving from the visco-
elastic properties of the arterial wall (Bonyhay, Jokkel, Karlocai,
Reneman,  Kollai, 1997; Kamenskiy et al., 2012). To the extent
that these wall movements are transmitted to the overlying skin,
the external carotid pulse signal can be taken as a close (although
not identical) surrogate of the central BP pulse. The waveform
shows a high degree of similarity with that of the ascending aortic
pulse (with only minor differences; C.-H. Chen et al., 1996; Kelly,
Karamanoglu, Gibbs, Avolio,  O’Rourke, 1989; Sztrymf, Jacobs,
Chemla, Richard,  Millasseau, 2013; Van Bortel et al., 2001). As
such, the carotid pulse waveform harbors a considerable amount of
information regarding myocardial and vascular dynamics. We have
shown that the texture of the carotid pulse waveform (recorded by
LDV) is indeed sufficiently rich to support biometric recognition
(M. Chen et al., 2010; Odinaka, O’Sullivan, Sirevaag,  Rohr-
baugh, 2015). More than a century of clinical research has identi-
fied in the carotid pulse multiple signs of CV disease (Nichols
et al., 2011; Perloff, 2000; Tavel, 1972). Of special importance
here is the information that is directly relevant to CV psychophysi-
ology—including heart rate, systolic time intervals, cardiac output,
arterial compliance, coronary perfusion, and impedance at the
microvasculature level (Nichols et al., 2011; N. Westerhof, Stergio-
pulos,  Noble, 2010).
There are precedents for using the carotid pulse in psychophy-
siological research. These include a series of studies by Obrist
et al., who proposed that the velocity representation (i.e., peak
slope of the upstroke, dP/dt) of the carotid pulse could serve as a
measure of extrinsic b-adrenergic influences on the myocardium.
The slope measure was observed to increase in such tasks as shock
avoidance and nonsignaled reaction time, and the effect was abol-
ished by the administration of the b-adrenergic blocking agent pro-
pranolol (Obrist et al., 1972, 1974). Questions were raised about
the quality of the dP/dt measure, with respect to the role of non-
neurogenic factors and its purity as a measure of b-adrenergic
effects, as well as methodological issues including the absence of
calibration (Heslegrave  Furedy, 1980; Light  Obrist, 1980;
Obrist  Light, 1980). The recording technique was cumbersome,
involving (a) attachment of a low-frequency microphone over the
carotid using an adhesive collar, (b) covering the microphone with
foam rubber to suppress extraneous noise, (c) holding the assembly
in place with an elastic bandage, and (d) using a neck collar to sta-
bilize the head and neck. Even at that, motion artifact proved to be
a limiting problem. An improved recording method involving fur-
ther stabilization of the head using a rigid frame has been described
by Ifuku et al. (Ifuku, Moriyama, Arai,  Shiraishi-Hichiwa, 2007;
Ifuku, Taniguchi,  Matsumoto, 1993, 1994; Moriyama  Ifuku,
2007, 2010) (and used in a series of studies involving cold pressor,
exercise, and other manipulations to validate the carotid dP/dt
pulse as a measure of inotropic influences), but in general the meth-
ods are not compatible with recording in behaviorally uncon-
strained conditions. More recently, hand-held or mounted
tonometric devices have been applied for this purpose (e.g., C.-H.
Chen et al., 1996; Edwards, Roy,  Prasad, 2008; Geleris, Stavrati,
 Boudoulas, 2004; LaFleche, Pannier, Lalaux,  Safar, 1998),
but shared limitations can be identified for all of these methods,
including the requirements for rigid assumption of a given postural
position as well as corruption of the signals of interest produced by
mechanical stimulation of the carotid baroreceptors (see Discus-
sion). As we show here, the noncontact LDV method may offer
significant advantages with respect to these issues.
The Range of Cardiorespiratory Interactions: Brief
Overview
The importance of respiration variables in psychophysiological
research is well established (Boiten, Frijda,  Wientjes, 1994;
Grossman  Taylor, 2007; Wientjes, 1992), as are the substantial
interactions among respiration and CV variables (see below). The
interactions take several forms, including direct effects of respira-
tion. These interactions are of sufficient clarity and robustness to
prompt frequent suggestions that they can be harvested for pur-
poses of monitoring ventilation parameters (Ahlstrom, Johansson,
Lanne,  Ask, 2004; Chua  Heneghan, 2006; De Meersman
et al., 1996; Johansson  Oberg, 1999; Lazaro, Gil, Bailon, 
Laguna, 2011; Nilsson, Johnsson,  Kalman, 2001; Orini, Pelaez-
Coca, Bailon,  Gil, 2011; Pitson, Sandell, van den Hout,  Stra-
dling, 1995). It follows that a full understanding of instantaneous
CV activity would require detailed appreciation of local respiratory
variables (M. T. Allen, Sherwood,  Obrist, 1986; Grossman,
1983). It also follows that many common situations that affect the
pattern of breathing (e.g., speaking, Beda, Jandre, Phillips,
Giannella-Neto,  Simpson, 2007; Winkworth, Davis, Adams, 
Ellis, 1995) as well as physical activity, skilled performance, anxi-
ety, and sleep will show respiration-dependent CV effects.
2 E.J. Sirevaag et al.
Cardiorespiratory interactions over the breathing cycle have
been reviewed extensively elsewhere (De Burgh Daly, 1986; Feihl
 Broccard, 2009a; Olsen, Tyson, Maier, Davis,  Rankin, 1985;
Robotham, 1988; Wise, Robotham,  Summer, 1981). The most
familiar form of such interactions in the psychophysiology litera-
ture is almost certainly respiratory sinus arrhythmia (RSA),
whereby interbeat interval (IBI) is observed to shorten during the
inspiration phase. Although RSA appears to be complexly deter-
mined, there is clear evidence for close association with respiration
(Brown, Beightol, Koh,  Eckberg, 1993; Denver, Reed,  Porges,
2007; Grossman  Taylor, 2007). There is a rich body of evidence
attesting to links between RSA and a range of variables including
development, affect, performance, and social cognition as well as
individual differences and clinical status (J. J. B. Allen, Chambers,
 Towers, 2007; Porges, 2007). It has been suggested that a way
of understanding RSA, as well as the much broader constellation of
respiration-related CV effects, is to consider them from a stand-
point of gas exchange efficiency, whereby oxygen and carbon
dioxide transfer is optimized by providing for greater volume and
exchange rate of blood in the pulmonary circuit during inhalation
(Galletly  Larsen, 1998; Giardino, Glenny, Borson,  Chan,
2003; Hayano  Yasuma, 2003; Hayano, Yasuma, Okada, Mukal,
 Fujinami, 1996; Yasuma  Hayano, 2004). Without regard to
the accuracy or completeness of this overview from a
“teleological” point of view (Tzeng, Sin,  Galletly, 2009), it does
provide a useful conceptual framework for categorizing the various
effects seen under conditions of normal spontaneous breathing.
This pulmonary vasocongestion during inspiration is key to under-
standing the pattern of hemodynamic effects. The displacement of
blood toward the pulmonary circuit appears to derive primarily
from enhanced venous return prompted by the fall in intrathoracic
pressure, and corresponding increases in right ventricular stroke
volume (SV) and ejection duration. The pooling of blood in the
lungs is facilitated by the high capacitance, low resistance, and
comparatively low pressure in the pulmonary arterial system
(Murgo  Westerhof, 1984; Rigolin, Robiolio, Wilson, Harrison,
 Bashore, 1995).
This relative displacement of blood during inspiration to the
pulmonary circuit has readily identifiable consequences in the sys-
temic circulation. Many of the effects can be traced to the reduction
in left ventricular SV (Andersen  Vik-Mo, 1984; Brenner 
Waugh, 1978; Cahoon, Michael,  Johnson, 1941; Caiani et al.,
2002; Davies et al., 2000; Gabe et al., 1969; Guz, Innes, 
Murphy, 1987; Karam, Wise, Natarajan, Permutt,  Wagner,
1984; Karlocai, Jokkel,  Kollai, 1998; Lauson, Bloomfield, 
Cournand, 1946; Robotham et al., 1978; Robotham  Mitzner,
1979; Robotham, Rabson, Permutt,  Bromberger-Barnea, 1979;
Ruskin, Bache, Rebert,  Greenfield, 1973; Toska  Eriksen,
1993)—a primary cause of which appears to be the reduction in
left ventricular diastolic filling (reduced preload). Other possible
mechanisms have been cited, including reduction in left ventricular
volume (associated with leftward bulge of the septum produced by
increased right ventricle filling), altered phase relationships of the
return from the lungs, and increase in left ventricular afterload
caused by negative pleural pressure and associated impediment on
ventricular ejection (Karam et al., 1984; Robotham, 1988; Robotham
 Mitzner, 1979). Reduced diastolic filling time associated with the
higher heart rate is another possible contributor (Toska  Eriksen,
1993), although relationships among hemodynamic variables and
RSA are complex (Elstad, Toska, Chon, Raeder,  Cohen, 2001;
Parati et al., 1987; Taylor  Eckberg, 1996). The magnitudes of the
SV effects are substantial. A detailed radionuclide ventriculography
study (Kim et al., 1987), for example, reported that left ventricular
stroke volume decreased during inspiration by 176 7%, in contrast
to an increase in right ventricular SV of 226 18%. The decrease in
left ventricular filling is also associated with a lengthening of pre-
ejection period (PEP) and decrease in left ventricular ejection time
(LVET; Johansson, Ahlstrom, Lanne,  Ask, 2006; Nandi, Pigott,
 Spodick, 1973; van Leeuwen  Kuemmell, 1987). Whereas RSA
appears to include a possible role of central oscillators or gating
mechanisms (Eckberg, 2003), the systemic hemodynamic effects
depend heavily on mechanical factors—as attested to by findings
that they are preserved with transplanted or otherwise denervated
hearts (Bernardi, Keller et al., 1989; Macor, Fagard, Vanhaecke, 
Amery, 1994; Zhang et al., 2002).
As would be expected given changes in SV of this magnitude, a
decrease in systolic blood pressure (SBP) during inspiration is rou-
tinely observed (Badra et al., 2001; Davies et al., 2000; Pitzalis
et al., 1998; Triedman  Saul, 1994). The periodic fluctuations in
SBP were correctly ascribed to respiration as long ago as 1828 by
Pouiselle (Larsen, Tzeng, Sin,  Galletly, 2010), with normal vari-
ation in humans ranging up to 10 mm Hg (beyond which the clini-
cally significant sign pulsus paradoxus is identified (Feihl 
Broccard, 2009b).
There are also consistent findings of peripheral effects, including
an increase in pulse transit time (PTT, or expressed as a decrease in
pulse wave velocity, PWV) during inspiration (Constant, Laude,
Murat,  Elghozi, 1999; Drinnan, Allen,  Murray, 2001;
Johansson et al., 2006; Pagani et al., 2003; Weltman, Sullivan, 
Bredon, 1964). The peripheral pulse amplitude is also affected.
Measured photoplethysmographically or using volume and flow-
metry methods, the peripheral pulse shows a reduction in pulse
amplitude during inspiration under normal breathing rates (Bernardi
et al., 1997; Bernardi, Rossi et al., 1989; Johansson  Oberg, 1999;
Martin, Marcellus, Sykowski,  Albany, 1938). As considered in
greater detail in the Discussion, there remains some uncertainty
regarding the extent to which these and other effects measured
peripherally (including possible rhythmicity in systemic vascu-
lar resistance) derive from macrocirculatory as opposed to local
vascular factors.
In the present study, the range of CV effects associated with the
respiratory cycle are exploited for purposes of validating the LDV
measures of CV function. Our effort is guided by the overall
hypothesis that the noncontact LDV method can be used to mea-
sure multiple, complex cardiovascular effects—related in this case
to the phase of the respiratory cycle as described above variously
for chronotropic, inotropic, and vascular effects. We demonstrate
how these measures are made accessible for study in minimally
constrained, behaviorally active situations—ones free from the bur-
den of invasive and obtrusive instrumentation or imposition of a
specific posture. The LDV measures are validated both within con-
text of the substantial existing literature, as well as a battery of con-
vergent measures obtained using conventional psychophysiological
methods. Our focus, again, is on the carotid pulse, measured using
the LDV method. To our knowledge, there have been few attempts
to study the respiration-related changes in the human carotid pulse
waveform, and those have focused on systolic time intervals
(Leighton, Weissler, Weinstein,  Wooley, 1971; Pigott  Spo-
dick, 1971; van Leeuwen  Kuemmell, 1987). Perhaps the most
detailed is our prior LDV study, in which a subset of the data uti-
lized here were analyzed using a hidden Markov model approach
to evaluate waveform features of the LDV carotid pulse (Kaplan,
O’Sullivan, Sirevaag, Lai,  Rohrbaugh, 2012). It was observed
that the state sequences showed periodic, orderly patterns that
Laser Doppler vibrometry for cardiovascular assessment 3
agreed in most cases with the respiration sequences. Consistent
with the effects described above, and evaluated in greater detail
here, the states associated with respiration phase differed in several
respects. LDV velocity pulses were observed to be of lower ampli-
tude during inspiration, and there were effects on systolic time
intervals. In the present report, we expand on these observations
and provide a more extensive background for interpreting them.
Method
Recording Protocol
Data were obtained during an initial 3-min resting baseline period,
as part of a larger experiment that involved a number of subsequent
activating maneuvers (not described here). Participants were seated
in a padded chair, with a soft foam pad (fashioned from a wheel-
chair head support) that loosely cradled the head to help maintain a
stable position. Breathing was entirely spontaneous. Participants
were instructed simply to sit quietly, but no instructions regarding
rate or constancy of breathing pattern were given.
Participants
Participants were recruited from the general community by adver-
tisement, and compensated $30 for participation in a single 2-h ses-
sion. All recruitment and testing procedures including the informed
consent process were approved by the Washington University
Human Research Protection Office. Participants were screened
using a telephone interview to be within the age range of 18–30
years, free of any physical disease (including hypertension, asthma,
and severe allergies), psychiatric disorder, or acute illness, non-
smokers, and not using any medication that might affect cardiores-
piratory functioning nor illicit substances. Of the 43 participants
enrolled, data from 11 were excluded from further analyses yield-
ing a final n 5 32 (19 female, mean age 24 [range 20–29], mean
height 172 cm [range 154–191], mean body mass index (BMI) 23.0
[range 18.0–29.0]). Reasons for exclusion were based on strain
gauge respiration signal of insufficient quality to support a detailed
breath-by-breath analysis (four), poor quality radial BP signal
(one), a protocol exception (one), marked irregularities in respira-
tion rate including occasional long expiratory pauses (three), and
minimal evidence for RSA in the spectral analysis of the electro-
cardiogram (ECG)-based heart rate series (two).
Physiological Recording
LDV recording. LDV data were obtained simultaneously from
two sites along the tract of the right carotid, which were identified
from visible landmarks. The approach was 458 to the right (approx-
imately radial to the neck skin surface at the carotid sites). The sites
were separated by 4 cm, with the upper site at the approximate
level of the carotid bifurcation. The bottom site was generally more
medial, consistent with our systematic mapping studies of the dis-
tribution of pulse amplitude along the carotid. The sites were
marked by applying an adhesive tape template, with 1-cm open cir-
cular patches (center to center separation5 4 cm). The exposed
skin at these patches was marked with a thin coating of titanium
dioxide, and dusted with retroreflective glass beads (45–63lm
diameter). The tape template was removed prior to data acquisition,
leaving the two treated skin patches. (Our current analysis capabil-
ities are effective with nontreated skin.) The lasers were targeted at
the centers of these two patches. Data from the top site were
obtained from a Polytec IVS-300 industrial vibrometer (band-pass
0.5Hz to 5 kHz, sensitivity5 5 mm/s/V), and from the lower site
with a colinear Polytec PSV-400 scanning vibrometer (band-pass 0
to 5 Khz, sensitivity5 10 mm/s/V). The laser standoffs were at dis-
tances corresponding to laser coherence maxima: 92.5 cm (IVS-
300) and 91.5 cm (PSV-400). The latency of the signal from the
IVS-300 was adjusted to compensate for the 1.1-ms output delay.
Data were sampled at a rate of 12.5 kHz using a Biopac MP150
system, with additional physiological and technical channels (see
below) integrated in a common file. Signals were variously down-
sampled and processed as described below.
Additional physiological signals. To serve as a basis for valida-
tion and interpretation, several conventional cardiorespiratory sig-
nals were obtained. Participants donned a loose scrub top and
disposable foam slippers to facilitate attaching these sensors.
 Respiratory effort was measured using a Biopac SS5B circum-
ferential belt, at a lower thoracic level.
 ECG was recorded from bilateral sites at the lower rib cage,
using a Biopac TEL100 amplifier.
 Impedance cardiogram (ICG) was measured using a Biopac
EBI100-C transducer (frequency 50Hz, gain 5 X/V) using
spot electrodes placed bilaterally on the neck, and lateral rib
cage (with the inner electrodes at the level of the xiphoid
process).
 Continuous BP was measured on a beat-to-beat basis using a
Colin 7000 (bundled with the Biopac system as the NIBP100).
This instrument measured the BP from the left radial artery on
a beat-to-beat basis, utilizing a tonometric principle.
 Photoplethysmographic pulse was obtained from the left ear
lobe, using a Biopac TDD200 transmission-based sensor and
PPG100C transducer.
 Dorsalis pedis pulse was recorded using a piezo film sensor
(Measurement Specialties LDT1-028K, 303 13mm) encased
in rubber and taped over the palpable pulse site on the left
foot, with constant pressure supplied by a thin, slightly com-
pressed foam block.
 Ballistocardiogram (BCG) was recorded from a sheet of piezo
film sensor (Pennwalt Kynar T052B/L) that was incorporated
into the chair cushion.
 Phonocardiogram (PCG) was recorded using a Biopac SS17
microphone, which was taped at a site just to the left of the
sternum over the third intercostal space.
Data Analysis
Segmentation by phase of respiration cycle. The analysis strat-
egy entailed binning pulses according to the phase in the associated
strain gauge respiration effort signal. The respiratory cycle was
divided into four algorithmically identified bins, as illustrated for a
fragment of a typical respiratory effort signal in Figure 1. The accu-
racy of the segmentation algorithm was visually confirmed for all
individual breaths. The first step involved identification of the
points of inspiration and expiration peaks. The amplitude of each
breath was then computed as the difference between successive
inspiration peaks (IP) and expiration peaks (EP). The correspond-
ing bins were then identified as the areas bounding the peaks where
the value of the respiration signal was within 10% of the IP or EP.
Periods defined as inspiration active (IA) and expiration active
(EA) were then identified as the regions between corresponding
transition points. Individual cardiac beats were assigned to these
4 E.J. Sirevaag et al.
bins on the basis of the moment of upstroke of the carotid LDV
pulse (the B point—see below).
Processing of LDV signals. The LDV pulses were analyzed
using custom methods developed within a MATLAB (MathWorks)
environment. The first stages involved suppressing speckle dropout
artifacts (an inherent property in the reflection from a coherent
source) and downsampling to 1000 Hz after antialias filtering. The
speckle dropout artifact suppression algorithm involved detection
of aphysiologically large and abrupt transitions in the signal (from
the 2nd derivative of the native velocity signal), removing the
affected points and resampling through the gaps in the original
velocity signal using linear interpolation. Only on rare occasions
was more than one consecutive point affected. The IVS-300 signal
was inverse filtered (Elbert  Rockstroh, 1980) to reverse the
effects of the original 0.5 Hz HP filter in the IVS-300 output (which
was modeled as a first-order Butterworth filter). The accuracy of
this procedure was confirmed in a test in which the inverse filtered
signal was directly compared with a simultaneous PSV-400 signal
(band-pass flat to DC) from the same target site. After additional
low-pass digital filtering at 150 Hz, the native velocity signal was
variously differentiated to assist in landmark identification, and
integrated to develop a displacement signal comparable to the pres-
sure pulse. Individual LDV pulses were detected and localized
independently of the ECG signal (i.e., based solely on features
intrinsic to the LDV signal), using an algorithm that focused on
detection of high-frequency bursts associated with carotid vessel
sounds as well as gross pulse waveform features—then identifying
the zero crossing of a tangent line fitted through the ascending
peak in the differentiated waveform to identify the moment of the
upstroke (labeled here the B point, reflecting the equivalent land-
mark in the ICG signal). Individual detections were reviewed and
edited as appropriate. Most of the analyses presented below (with
exceptions noted) were based on the signal from the distal (IVS-
300) laser, from which 98% (range 91% to 100%) of the total avail-
able beats (as determined from the gold standard ECG signal) were
used in the analyses. The number was reduced in the selected anal-
ysis entailing both lasers (see below) to 92% (range 42% to 100%,
with all but one participant exceeding 70%). The incisura (x point,
corresponding to the end of systole) was identified in terms of a
characteristic inflection in the differentiated velocity signals, quali-
fied with respect to normative data for LVET (Polis, Smets,  De
Keyser, 1974; Willems  Kesteloot, 1967). The displacement sig-
nals were detrended prior to extracting additional measures, to sup-
press slow artifacts associated with respiration and other gross
movement, using a procedure whereby the trend line developed
from a cubic spline fitted through the signals at the B points was
subtracted (in accord with procedures sometimes applied for cor-
rection of ECG baseline wander; Baldilini, Moss,  Titlebaum,
1991; Meyer  Keiser, 1977).
LDV pulse waveform analyses. The general form of the LDV
pulses is illustrated in Figure 2, where the waveforms have been
selectively averaged according to segment of the respiration cycle.
They have also been grand averaged over all 32 participants, after
Figure 2. Grand-averaged LDV velocity (left) and displacement (right) pulses, for the four respiration cycle bins. Waveforms have been linearly time
warped, to compensate for individual differences in IBI durations.
Figure 1. Illustration of binning procedure for analyzing the associated
cardiovascular signals according to phase of the respiration cycle. Each
cycle is divided algorithmically into four segments, corresponding to
inspiration active (IA; black), inspiration peak (IP; blue), expiration
active (EA; red) and expiration peak (EP; green).
Laser Doppler vibrometry for cardiovascular assessment 5
linear time-warping to preserve the waveform features in the face
of appreciable individual differences in IBI. For purposes of this
illustration only, the longest mean IBI (for the EP segment) is arbi-
trarily set to 1,000 points, with the B point (upstroke) at time 0 and
preceded by 200 points. Waveforms for the other respiration seg-
ments are similarly warped, but with the respective durations pre-
served relative to the EP waveform. All waveforms are truncated at
the time corresponding to the B point of the succeeding beat. At
the left is the native velocity signal; in the right panel this has been
integrated to depict a displacement signal that, for reasons
described above, is broadly comparable to the pressure signal. On
average, the peak mechanical displacement is small—on the order
of 0.23mm and with important features in the lm range. The
waveforms can be separated into phases of systole (from time 0
through the incisura at about point 300) and diastole. The B point
determined algorithmically (time 0) appears somewhat anoma-
lously to follow slightly the onset of the upstroke, but the desig-
nated point can be determined with much greater reliability and
stability than when based on attempts to detect the earliest inflec-
tion point. Several additional key features are worthy of mention.
These include the observation that for the EP phase the IBI is lon-
gest (consistent with the directionality of RSA), the time of the
incisura (at about 300 ms) is latest, and the peak amplitude as well
as the amplitude during late systole is also greatest. Findings during
the IP phase mirror these effects, as presented in greater detail
below.
Wave separation analysis. There have been many attempts over
the last century to develop measures of stroke volume from the
carotid pressure pulse contour (and, more recently, from peripheral
sites including the radial artery and finger; see Mukkamala  Xu,
2010; Thiele  Durieux, 2011, for recent reviews). Early attempts
were generally based on measures incorporating signal aspects
throughout the duration of systole, for example, averaged ampli-
tude or area measures. These methods were improved somewhat
by emphasizing measures only through midsystole, recognizing
that late systole in particular will include pressure waves reflected
from distal sites. These reflections will increase the amplitude of
the pressure pulse late in systole, but will serve to impede forward
flow thus creating a period of inverse relationship between pulse
amplitude and forward flow.
Even more recent is the recognition that measures of carotid
flow should be based principally on a period early in systole.
Reports describing carotid blood flow are in agreement in showing
that the peak flow rate occurs early in systole, rapidly diminishing
in later systole (Brands, Hoeks, Hofstra,  Reneman, 1995; Holds-
worth, Norley, Frayne, Steinman,  Rutt, 1999; Rabben et al.,
2004). Studies in which both blood flow and pressure are simulta-
neously considered illustrate that, for a brief period during the sys-
tolic upstroke, the pressure and flow contours are identical (for
perhaps the initial 20–30% of systole), but thereafter diverge rap-
idly as the pressure contour is elevated by reflected pressure fronts
(Khir, O’Brien, Gibbs,  Parker, 2001; Niki et al., 2002; Parker,
2009; Rabben et al., 2004). The reflection waves later in systole are
often readily identifiable as distinct components, particularly in
older patients. The specific loci of the reflection sites is a matter of
debate, but they likely include widely distributed impedance sour-
ces in the arterioles, with possible contributions from major discon-
tinuities such as the termination of the abdominal aorta (Murgo,
Westerhof, Giolma,  Altobelli, 1980; O’Rourke, 1982). The
appearance of reflected pressure waves early in systole suggests
that they possibly originate in the impedance breach in the cerebral
microvasculature (Bleasdale et al., 2003), or perhaps in some early
impedance discontinuity in the central elastic vessels. In any case,
the close flow/pressure relationship is restricted to a brief period.
In the absence of direct measures of flow (i.e., with the avail-
ability of only the pressure signal, as is the case with the LDV
method), a surrogate measure of flow can be extrapolated from the
pressure pulse measured early in systole. This general approach
was proposed by Westerhof et al. (B. E. Westerhof, Guelen, West-
erhof, Karemaker,  Avolio, 2006), who suggested that reflection
activity be extracted with respect to a triangular equivalent flow
waveform. The peak of this waveform was determined as an inflec-
tion point located early in the pressure pulse upstroke, or (in the
absence of a clearly demarcated inflection point) a point on the
pulse waveform located at 30% of the LVET. The triangle was
shown to be a reasonable approximation of aortic flow (albeit in a
patient group). Subsequent refinements of the method have demon-
strated the benefits of using a physiologically more plausible model
of flow, based on either modeled actual flow waveforms (J. G.
Kips et al., 2009; Zamani et al., 2014) or on estimated flow wave-
forms based on a Windkessel-based modeling approach (Hametner,
Weber, Mayer, Kropf,  Wassertheurer, 2013; Pucci, Hametner,
Battista, Wassertheurer,  Schillaci, 2015; Weber et al., 2012). In
parallel, the importance of deriving a purified measure of wave
reflections must also be emphasized. The most common method
for identifying the signs of increased resistance and vascular stiff-
ening (as occurs, e.g., with aging) from the pressure pulse wave-
form is to compute a ratio comparing the amplitude of the initial
systolic upstroke peak (thought to reflect forward flow) with the
“augmentation” of this peak by the addition of a later systolic peak
produced by the return of the reflected pressure wave(s) (Nichols
et al., 2011). This ratio is typically referred to as the augmentation
index (AIx). In practice, this technique is sometimes affected by
difficulties identifying key landmarks, or achieving a meaningful
separation between the overlapped constituent waves.
Our implementation adopted a pseudoflow waveform influ-
enced by the parametric description of the carotid flow waveform
provided by Holdsworth et al. (1999), informed also by estimates
of peak aortic flow based on the ICG signal (Kubicek et al., 1974).
The initial steps were to compute grand mean ensemble averages
of both the LDV and ICG waveforms for each individual,
synchronized to the LDV B point. An early segment of the LDV
pulse waveform (B point through 125 ms) was cross-correlated
over the early ICG waveform, lagged in 1-ms steps, to identify the
optimal temporal adjustment required to synchronize the upstrokes
of the ICG and LDV pulse waveforms. The peak of the ICG wave-
form was then transcribed on to the LDV pulse waveform and
accepted provisionally as the moment of peak flow (in the grand
mean waveforms). In a subsequent step, this point was modeled
and adjusted solely from features inherent in the LDV pulse wave-
form (to support other applications in which the ICG signal might
not be available). The approach was regression based, incorporat-
ing LVET, as well as values at inflection points in the LDV pulse
upstroke identified from higher-order derivatives of the displace-
ment signal. The mean values for the times of this peak were in the
range of 22–23% of LVET—times somewhat earlier than 30% sug-
gested by B. E. Westerhof et al. (2006). A final step involved fit-
ting the pseudoflow model (normalized in amplitude to the LDV
displacement pulse at the time of peak pseudoflow) to eight key
points derived from a published model of carotid blood flow devel-
oped from Doppler ultrasound measures (Holdsworth et al., 1999),
as illustrated in Figure 3 (left). Specifically, amplitude measures
were taken at times corresponding to
6 E.J. Sirevaag et al.
(a) the B point,
(b) the peak of the first derivative of the upstroke,
(c) half the time on the upstroke from the B point to the maxi-
mum flow point,
(d) the maximum flow point identified from the regression
estimate,
(e) a point on the descending pseudoflow waveform mirroring
point (c) identified above,
(f) a point on the descending pseudoflow waveform mirroring
point (b) identified above,
(g) a point extending an additional 10% of the remaining time to
the x point (i.e., incisura), and
(h) the x point marking the end of the LVET and the presumed
cessation of flow.
A cubic spline interpolation was then used to join these points
in a smoothed pseudoflow waveform.
Following development of the pseudoflow waveform, wave
separation was accomplished using the procedure described by B.
E. Westerhof et al. (2006), whereby
forward wave 5 ðLDV carotid pulse
1 pseudo 2 flow waveÞ=2
backward wave 5 ðLDV carotid pulse
2 pseudo 2 flow waveÞ=2
The resultant decomposition is illustrated in Figure 3 (right).
The decomposed forward and backward waves were in turn ana-
lyzed to extract key features, utilizing lagged cross-correlation
and based on the assumption that the backward wave compo-
nents will be rough copies (although changed in amplitude and
timing) of the forward wave. This procedure routinely yielded
separate mid- and late systolic peaks in the backward wave (in
addition to diastolic features that are not reported here). The
amplitude of each of these peaks was computed, and used to
form an augmentation ratio in terms of the ratio with the forward
wave peak amplitude.
The procedures described above were applied both on a beat-to-
beat basis (with derived measures averaged over the respective
ensemble of beats) as well as ensemble averages of the waveform
data. These two sets of analyses produced generally equivalent
results; the LDV measures reported below are based on the beat-to-
beat analyses. The analyses of complementary psychophysiological
signals (described below) were based on the identical ensembles of
beats used for purposes of LDV analysis.
Analysis of conventional signals. Standard methods were uti-
lized to measure the ICG signal, including low-pass filtering at
20 Hz, differentiation, and inversion, and measurement of the B
and x points utilizing comparable algorithms as developed for the
LDV signal (described above). SV was estimated following the
method of Kubicek et al. (Kubicek, Patterson,  Witsoe, 1970; uti-
lizing LVET derived from the LDV pulse waveform). Additional
methods are described below.
Statistical analysis. The presentation of data is based largely on
means and associated confidence intervals (CIs), emphasizing com-
parison of values at the IP and EP times, where the maximum dif-
ferences were nearly always observed. CI levels of 95% are
provided, one-tailed since the direction of effect could in all cases
be hypothesized based on the strength of existing evidence. In the
absence of accepted gold standard measures (usually invasive), we
have not made extensive use of methods for formally evaluating
concordance among the LDV and conventional measures (Critch-
ley, Lee,  Ho, 2010).
Results
Respiration Parameters
Respiration rate was analyzed using both time-domain and
frequency-domain methods, the results of which agreed closely.
The time-domain measure was derived by calculating separately
the median time separating consecutive inspiration peaks, and then
separating consecutive expiration peaks and averaging those val-
ues. This analysis yielded a mean respiration rate of 13.8 breaths
per min (SD5 3.7, range 7.0 to 19.2). All but five participants had
mean respiration rates 10 breaths per min. The finding of sub-
stantial individual differences in rate of spontaneous breathing (as
Figure 3. Illustration of wave separation procedures used to decompose LDV displacement pulse into forward (FWD) and backward (BWD) constitu-
ents. Left: Original displacement pulse (black) and the pseudoflow wave formed from eight points identified from waveform features. Right: Decom-
posed forward wave (blue) and backward wave (green).The end of systole (incisura) is marked by the vertical hash mark.
Laser Doppler vibrometry for cardiovascular assessment 7
well as signs of nonstationarity) is in agreement with prior observa-
tions (Goodman, 1964; Lenfant, 1967).
CV Measures
Heart rate. Heart rate, as assessed from the LDV and the ECG
signals, was analyzed variously using a number of frequency- and
time-domain approaches, which agreed in confirming a close corre-
spondence between rate measures obtained using the two signals.
The presentation here is focused on IBI measures and their relation-
ship to phase of the respiratory cycle. The character of the IBI
measures derived from the LDV signal is of fundamental impor-
tance, since many of the CV measures are keyed to the LDV
B-point measure, and because the accuracy of the B-point measure
confirms the effectiveness of the LDV method if used in the
absence of conventional contact-based methods for measuring heart
rate.
The IBI measures derived from the ECG and LDV signals are
shown in the first two rows of Table 1, which confirms that the IBI
was significantly shorter during the IP than EP phases for both sig-
nals, with intermediate values during the transition phases (IA and
EA). The IBI effect for the LDV signal is also illustrated, along
with other key LDV findings, in Figure 4.
The levels of agreement between IBIs measured from the LDV
and ECG signals were assessed using Bland-Altman statistics
(Bland  Altman, 1986). Genuine minor differences would be
anticipated, since the LDV B points will include some contribution
from PEP and from aortic/carotid PTT, which, as shown below, are
systematically affected by phase of the respiration cycle but which
are not reflected in ECG-based measures. As is clear in Figure 5,
however, the PEP and PTT effects are proportionately small, and
overall the agreement between LDV- and ECG-based IBI measures
is very high. The correlation between mean values at each of the
four respiration phases exceeded r 5 .99, and the bias never
exceeded 3 ms. The level of agreement between LDV- and ECG-
based measures of IBI attests to the effectiveness of the LDV anal-
ysis algorithms.
Systolic time intervals: LVET and PEP. The LVET measured
from the LDV pulse, as the time from the B point to the x point,
agreed with the parallel measure in the ICG signal in showing sig-
nificantly shorter times during IP than in EP, although the absolute
durations were about 17 ms shorter for ICG-based measures (Table
1, Figure 4). The origins of this difference likely lie in the algorith-
mic approaches to identifying the B and x points in the respective
signals. PEP was measured variously as the time from the ECG
R-wave peak to the ICG B point, the parallel time to the LDV
carotid pulse B point, and, more traditionally (Weissler, 1983), as
the time to the onset of the second heart sound (S2) minus LVET
Figure 4. Graphic illustration of effects of respiration cycle phase on
several key CV measures, for the inspiration active (IA), inspiration
peak (IP), expiration active (EA) and expiration peak (EP) phases.
Shown are the proportionate values at each phase in comparison to the
overall mean, and associated 95% CIs. (See Table 1–5.) The same verti-
cal scale applies to all measures.
Table 1. Mean Values and Effects of Phase of Respiration Cycle for Chronotropic Measures
Chronotropic measures n Mean (raw units) IA Prop [ 6 CI] IP Prop [ 6 CI] EA Prop [ 6 CI] EP Prop [ 6 CI] EP minus IP [ 6 CI]
IBI – ECG R to R 32 837.3 ms 1.000
[0.994, 1.006]
0.951
[0.943, 0.959]
1.002
[0.995, 1.009]
1.048
[1.040, 1.056]
0.097
[0.081, 0.113]
IBI – LDV Bpnt to Bpnt 32 837.3 ms 1.004
[0.997, 1.010]
0.948
[0.940, 0.956]
0.999
[0.991, 1.006]
1.050
[1.042, 1.058]
0.102
[0.086, 0.118]
LVET – LDV 32 272.9 ms 0.995
[0.991, 0.999]
0.975
[0.971, 0.979]
1.012
[1.008, 1.016]
1.018
[1.015, 1.021]
0.043
[0.035, 0.050]
LVET – ICG 32 255.6 ms 0.990
[0.984, 0.996]
0.973
[0.968, 0.978]
1.019
[1.013, 1.025]
1.018
[1.013, 1.023]
0.045
[0.036, 0.055]
R to LDV Bpnt latency 32 108.0 ms 1.003
[0.997, 1.009]
1.036
[1.028, 1.044]
0.990
[0.982, 0.997]
0.971
[0.966, 0.977]
-0.065
[-0.078, -0.051]
PEP – R to ICG Bpnt 32 92.8 ms 1.005
[0.997, 1.013]
1.031
[1.025, 1.038]
0.987
[0.979, 0.995]
0.977
[0.971, 0.983]
-0.054
[-0.066, -0.042]
PEP–- R to S2 minus
LVET
30 79.3 ms 0.996
[0.988, 1.004]
1.048
[1.032, 1.065]
0.997
[0.988, 1.007]
0.959
[0.946, 0.971]
-0.089
[-0.117, -0.062]
Note. Shown in Column 3 are the mean values, averaged over all four phases. The values in Columns 4–7 are expressed as proportions of the mean
values, for the inspiration active (IA), inspiration peak (IP), expiration active (EA), and expiration peak (EP) phases, and associated 95% CIs. The val-
ues in the right column are differences between EP and IP, and associated CIs, which in no case includes zero.
8 E.J. Sirevaag et al.
(extracted here from the LDV pulse). (The latter measure was
based on 30 participants, because the phonocardiographic S2 onset
could not be reliably measured in two participants. For purposes of
this analysis, the S2 measurement was based on a high-pass filtered
[70 Hz] and rectified version of the microphone signal.) All meas-
ures concurred in showing a significant increase in PEP during the
IP phase of the respiration cycle, although there was some variabil-
ity among them in absolute values, probably incorporating respec-
tive contributions from aortic and carotid transit times as well as
measurement error.
BP and inotropic CV effects. Consistent with prior findings,
measures of BP, including systolic, diastolic, mean arterial, and
pulse BPs, were observed to be lower during IP than EP (Figure 4,
Table 2). The effects on diastolic BP were quantitatively much
smaller, and differed from all other measures insofar as there was
some indication of a phase shift whereby the greatest differences
were seen during the active rather than peak respiration phases.
The amplitude of the LDV displacement pulse, taken at the time of
the peak pseudoflow wave, was substantially reduced, as was a
measure of LDV stroke volume computed as the product of that
amplitude and LVET (using procedures analogous to those used in
the analysis of the ICG waveform). It should be qualified that the
LDV stroke volume measure is uncalibrated with respect to actual
flow volume. A measure of LDV cardiac output, formed as the
product of the LDV stroke volume and heart rate, was not signifi-
cantly affected by phase of the respiration cycle, consistent with
prior observations (Toska  Eriksen, 1993) that the two contribut-
ing variables tend to offset each other to maintain a steady cardiac
output over the respiration cycle. Similar to the pulse amplitude at
the time of the pseudoflow peak, other measures of the LDV ampli-
tude, including peak LDV velocity and its ratio to displacement
pulse amplitude (dP/dt)/P, were smallest during IP and largest dur-
ing EP phases.
The ICG SV measure, in comparison, was not significantly
affected by respiration phase, and in fact showed a slight,
Figure 5. Bland-Altman analysis comparing mean LDV- and ECG-based IBI measures, at each of the four phases of the respiration cycle. The Bland-
Altman plots were visually nearly identical if the gold standard ECG IBI rather than the mean (ECG1LDV)/2 was used for the abscissa.
Laser Doppler vibrometry for cardiovascular assessment 9
anomalous tendency to be largest during the IP phase. In view of
the overwhelming evidence that stroke volume is reduced during
inspiration (see the introduction), this would point to a limitation
pertaining to use of ICG for the study of cardiorespiratory interac-
tions (see Discussion)—and for this reason we avoid here present-
ing derived measures including ICG-based measures of cardiac
output or systemic resistance.
Consistent with evidence reviewed earlier, the peak-to-peak
amplitudes of the peripheral pulse at the ear lobe (measured photo-
plethysmographically) and the dorsalis pedis artery (measured with
a mechanical sensor) were substantially smaller during the IP
phase. Because of signal quality issues, the dorsalis pedis pulse was
based on ensemble averages, from n 5 25 participants.
BCG effects. The BCG data are presented in some detail here
because of the method’s potential utility as a complementary or
stand-alone nonobtrusive assessment modality, involving only inci-
dental contact with the chair cushion. The basic form of the BCG
signal is illustrated in Figure 6. These data have been averaged
over all respective candidate beats within a 3-min resting period,
and grand averaged over 32 participants. In accord with the con-
ventional nomenclature, the major inflections are labeled, respec-
tively, as the H, I, J, and K waves. These features show a general
homology with the illustrations in the literature, although rigorous
identity is complicated by substantial differences in recording
method, including body position and sensor characteristics. Meas-
ures of the I and J waves could be obtained with highest confi-
dence, and are reported here. Overall, the respiration-related effects
agree with effects that have been reported previously using clinical
instruments (Dinaburg  Zuckerman, 1984; Starr  Friedland,
1946). At IP, the latencies of the key components are reduced, and
the peak-to-peak amplitudes are increased. These effects are evi-
dent in Figure 6 and are depicted quantitatively in Table 3.
Augmentation ratio CV effects. Computation of the augmenta-
tion ratio was based on the decomposition of the LDV displace-
ment pulse waveforms into the forward and backward components,
using the methods described above. The amplitude of the forward
component was taken at the time of the peak (which, as described
Table 2. Mean Values and Effects of Phase of Respiration Cycle for Blood Pressure and Inotropic Measures
Blood pressure and
inotropic measures n
Mean
(raw units)
IA Prop
[ 6 CI]
IP Prop
[ 6 CI]
EA Prop
[ 6 CI]
EP Prop
[ 6 CI]
EP minus IP
[ 6 CI]
BP systolic 32 111.097 mmHg 0.995
[0.991, 0.999]
0.969
[0.962, 0.976]
1.011
[1.007, 1.016]
1.025
[1.019, 1.031]
0.056
[0.043, 0.069]
BP diastolic 32 63.894 mmHg 0.990
[0.987, 0.993]
0.994
[0.989, 0.998]
1.010
[1.005, 1.014]
1.007
[1.002, 1.011]
0.013
[0.004, 0.022]
BP mean arterial
pressure
32 95.363 mmHg 0.994
[0.990, 0.997]
0.974
[0.968, 0.981]
1.011
[1.007, 1.015]
1.021
[1.016, 1.026]
0.047
[0.035, 0.058]
BP pulse pressure 32 47.203 mmHg 1.000
[0.992, 1.008]
0.935
[0.923, 0.948]
1.015
[1.006, 1.024]
1.049
[1.040, 1.059]
0.114
[0.092, 0.135]
LDV pulse amp (flow
point)
32 0.238 mm 1.011
[1.003, 1.019]
0.953
[0.933, 0.974]
0.995
[0.980, 1.009]
1.041
[1.027, 1.055]
0.088
[0.054, 0.121]
LDV SV 32 65.126 mm.ms 1.007
[0.999, 1.015]
0.929
[0.907, 0.951]
1.008
[0.992, 1.024]
1.056
[1.041, 1.072]
0.127
[0.090, 0.163]
LDV CO 32 4640.689
mm.ms.HR
1.006
[0.995, 1.016]
0.980
[0.962, 0.999]
1.007
[0.990, 1.023]
1.007
[0.994, 1.020]
0.027
[20.004, 0.057]
LDV velocity peak amp 32 5.872 mm/s 1.018
[1.005, 1.032]
0.924
[0.899, 0.949]
1.011
[0.992, 1.030]
1.046
[1.030, 1.063]
0.123
[0.082, 0.164]
(dP/dt)/P 32 24.941 /s 1.001
[0.992, 1.010]
0.970
[0.951, 0.989]
1.017
[1.005, 1.028]
1.012
[0.997, 1.027]
0.042
[0.008, 0.075]
SV ICG 32 91.046 ml 1.006
[0.999, 1.014]
1.011
[0.997, 1.024]
0.990
[0.981, 0.999]
0.993
[0.984, 1.003]
20.017
[20.040, 0.005]
Ear pulse amp (P-P)
(arbitrary units)
32 91.46 0.951
[0.918, 0.985]
0.841
[0.804, 0.878]
1.092
[1.051, 1.132]
1.116
[1.087, 1.145]
0.275
[0.210, 0.340]
Dorsalis pedis amp
(P-P) (arbitrary units)
25 133.993 1.009
[0.996, 1.023]
0.953
[0.939, 0.967]
1.001
[0.990, 1.013]
1.036
[1.025, 1.047]
0.083
[0.060, 0.106]
Note. Shown in Column 3 are the mean values, averaged over all four phases. The values in Columns 4–7 are expressed as proportions of the mean
values, for the inspiration active (IA), inspiration peak (IP), expiration active (EA), and expiration peak (EP) phases, and associated 95% CIs. The val-
ues in the right column are differences between EP and IP, and associated CIs, which in no case except the two indicated in italicized text (LDV CO,
SV ICG) includes zero.
Figure 6. Grand-averaged ballistocardiogram (BCG) signals obtained
from the piezo pad in the chair cushion, for the inspiration active (IA),
inspiration peak (IP), expiration active (EA) and expiration peak (EP)
phases of the respiration cycle. Measures of amplitude and latency were
most reliably obtained from the I and J waves. Signals are synchronized
to the ECG R wave peak, at time 0.
10 E.J. Sirevaag et al.
above, corresponded with the time of the peak of the pseudoflow
waveform). The amplitude peaks of the backward waveform were
taken at the times of the mid- and late systolic peaks identified
using the lagged correlation methods described above. Augmenta-
tion ratios computed for both peaks (especially the late systolic
peak) showed a decrease during the IP phase (Figure 4, Table 4). A
separate measure based on the integrated areas of the forward and
backward waves (during systole) yielded similar results.
Arterial timing effects. Pulse transit times were computed vari-
ously from the B point of the LDV displacement pulse to the
upstroke of the pulses at the ear lobe (photoplethysmographic), the
radial artery (mechanical), and dorsalis pedis artery (mechanical).
All showed evidence of longer latencies during the IP phase
(Figure 4, Table 5), in agreement with prior evidence. PTT meas-
ures were taken from the LDV B point (rather than the ECG
R wave) to eliminate any contribution from PEP (Newlin, 1981). In
an effort to evaluate possible local respiration-related effects on
pulse wave velocity along the carotid artery, the timing of the LDV
displacement pulses at the proximal and distal sites (separated by
4 cm) was compared. Different analysis approaches, based on times
of the B points, x points, peaks of the velocity pulses, and lagged
covariance of the systolic velocity waveforms varied somewhat
with respect to absolute timing differences, but all produced gener-
ally comparable findings of longer transit times during the IP
phase. The values shown in Figure 4 and Table 5 are based on the
lagged covariance approach. The mean transit time over the 4 cm
separating the proximal and distal sites of 6.6 ms yields a carotid
pulse wave velocity of slightly more than 6 m/s, generally consist-
ent with values determined using other methods (Brands et al.,
1995; Hermeling, Reneman, Hoeks,  Reesink, 2011; Rabben
et al., 2004). Latencies of the mid- and late systolic peaks in the
backward wave were also analyzed, motivated by indications that
the timing of the backward wave might serve as estimates of pulse
wave velocity, in the form of round trip travel time (RTT; J. G.
Kips et al., 2009; Qasem  Avolio, 2008). These RTT timing
measures were not effective in this application, showing the
absence of any significant effect for the midsystolic peak and a sig-
nificant effect for the late systolic peak, but in the opposite direc-
tion to that shown by conventional PTT measures.
Discussion
The data presented here support the general effectiveness of the
LDV-recorded carotid pulse as a method for measuring multiple
aspects of CV function. The findings confirmed that several critical
variables relating to chronotropic, inotropic, and vascular function
could be measured in the LDV pulse—and that these measures
agreed in most respects with expectations derived from the substan-
tial literature regarding the effects of the respiration cycle on CV
function and with convergent data obtained with conventional psy-
chophysiological methods. The LDV measures were obtained on a
noncontact basis, and the key features could be extracted algorith-
mically. These findings, in turn, suggest that the LDV carotid pulse
contour can be further exploited utilizing a range of analysis meth-
ods that have been shown to be predictive of important clinical end
points, including a variety of time- and frequency-domain model-
ing approaches (Laurent et al., 2006). Several interpretive consider-
ations and limitations that apply to these findings are presented
briefly below.
Cardiorespiratory Interactions
The spontaneous breathing condition studied here is not without its
issues (Ritz, 2009), but it does reliably produce signs of cardiores-
piratory interaction that agree closely with those if breathing is
paced at the same frequency (Bloomfield et al., 2001). In view of
the inescapable variability in breaths from one cycle to the next,
Table 3. Mean Values and Effects of Phase of Respiration Cycle for Ballistocardiogram Measures
BCG measures n
Mean
(raw units)
IA Prop
[ 6 CI]
IP Prop
[ 6 CI]
EA Prop
[ 6 CI]
EP Prop
[ 6 CI]
EP minus IP
[ 6 CI]
R to BCG I wave latency 32 113.3 ms 1.024
[1.014, 1.033]
0.923
[0.907, 0.939]
1.006
[0.997, 1.016]
1.047
[1.033, 1.061]
0.124
[0.096, 0.151]
R to BCG J wave latency 32 167.9 ms 1.006
[0.996, 1.015]
0.985
[0.971, 1.000]
0.999
[0.985, 1.013]
1.010
[0.998, 1.023]
0.025
[0.002, 0.048]
BCG amplitude (I-J P-P)
(arbitrary units)
32 301.15 1.048
[1.025, 1.071]
1.144
[1.090, 1.198]
0.932
[0.898, 0.967]
0.876
[0.820, 0.931]
20.269
[20.373, 20.164]
Note. Shown in Column 3 are the mean values, averaged over all four phases. The values in Columns 4–7 are expressed as proportions of the mean
values, for the inspiration active (IA), inspiration peak (IP), expiration active (EA), and expiration peak (EP) phases, and associated 95% CIs. The val-
ues in the right column are differences between EP and IP, and associated CIs, which in no case includes zero. BCG 5 ballistocardiogram.
Table 4. Mean Values and Effects of Phase of Respiration Cycle for Augmentation Ratio Measures
Augmentation ratio n
Mean
(raw units)
IA Prop
[ 6 CI]
IP Prop
[ 6 CI]
EA Prop
[ 6 CI]
EP Prop
[ 6 CI]
EP minus IP
[ 6 CI]
Augmentation
ratio mid
32 0.732 0.994
[0.971, 1.016]
0.974
[0.943, 1.005]
0.981
[0.961, 1.001]
1.051
[1.026, 1.075]
0.077
[0.024, 0.130]
Augmentation
ratio late
32 0.724 0.934
[0.907, 0.962]
0.903
[0.857, 0.949]
1.028
[0.998, 1.058]
1.135
[1.097, 1.172]
0.231
[0.151, 0.311]
Augmentation
ratio total
32 1.456 0.967
[0.947, 0.987]
0.941
[0.910, 0.972]
1.004
[0.987, 1.022]
1.088
[1.063, 1.113]
0.147
[0.093, 0.200]
Note. Shown in Column 3 are the mean values, averaged over all four phases. The values in Columns 4–7 are expressed as proportions of the mean
values, for the inspiration active (IA), inspiration peak (IP), expiration active (EA), and expiration peak (EP) phases, and associated 95% CIs. The val-
ues in the right column are differences between EP and IP, and associated CIs, which in no case includes zero.
Laser Doppler vibrometry for cardiovascular assessment 11
spontaneous breathing poses difficulties for frequency-based meth-
ods and limitations on the resolution with which phase relation-
ships can be determined (although these problems may not be
intractable, e.g., Orini, Laguna, Mainardi,  Bailon, 2012). These
issues notwithstanding, the procedure used here, involving binning
according to phase of the respiration cycle, was adopted because of
its simplicity, and indeed it proved to reveal highly significant
effects in multiple measures.
The typicality of the data obtained here was confirmed by the
compatibility with previously reported findings (as reviewed in the
introduction). These included, during inspiration, the cardinal signs
of fall in BP and decrease in IBI. The IBI measures were nearly
equivalent, whether based on the conventional R wave of the ECG,
or on algorithmically detected times of LDV carotid pulse
upstroke, attesting to the effectiveness of the algorithms for proc-
essing the LDV signal. LVET was also reduced by similar amounts
in both the LDV carotid pulse and ICG signals (Frey  Doers,
1983). The LVET reduction during inspiration (in conjunction with
lengthening of the right ventricular ejection time; Leighton et al.,
1971; van Leeuwen  Kuemmell, 1987) produces the splitting of
the S2 heart sound that is freely auscultated in the clinical exam
(Castle  Jones, 1961; Ehlers, Engle, Farnsworth,  Levin, 1969;
Felner, 1990; Yildrim  Ansari, 2007)—and which was readily
identifiable here in the phonocardiograms of 23 of the 32 partici-
pants. The pattern of inspiratory decrease in LVET (Karlocai et al.,
1998; Nandi et al., 1973; van Leeuwen  Kuemmell, 1987), and
increase in left ventricular PEP (Johansson et al., 2006; Nandi
et al., 1973; van Leeuwen  Kuemmell, 1987), is commonly attrib-
uted to reduced left ventricular filling as would apply during the
inspiration phase of the respiration cycle.
The attempt to derive measures related to changes in SV from
the pressure pulse follows an extensive tradition of similar efforts
(Mukkamala  Xu, 2010; Thiele  Durieux, 2011), but as imple-
mented here might offer the advantage of being based on the puri-
fied forward wave component of the pressure pulse, and thus
relatively unaffected by reflected pressure fronts in the backward
wave. The derived measure of LDV SV responded in the antici-
pated manner; the finding of a 13% change over the respiration
cycle is in line with findings by other investigators using other
methods, although the specific magnitude of the effect on SV is
affected by the rate and depth of respiration (Guz et al., 1987).
In this regard, it is notable that the ICG method did not provide
evidence of the anticipated reduction in SV during inspiration;
indeed, there was a small (and nonsignificant) anomalous trend in
the opposite direction. We obtained a similar pattern in the face of
several attempts at filtering or detrending the gross pneumoimpe-
dance disturbances from the ICG signal (Hahn, Sipinkova, Baisch,
 Hellige, 1995). The impact of respiration on ICG measures is
widely recognized and is often considered a source of artifact that
is typically dealt with by restricting sampling to beats falling in
periods of end-expiratory eupnea (Miller  Horvath, 1978), by
ensemble averaging across the respiratory cycle (Ekman et al.,
1990; Muzi et al., 1985), or by attempts at filtering and adaptive
cancellation (Pandey  Pandey, 2005; Raza, Patterson,  Wang,
1992; Yamamoto et al., 1988). The small increase in ICG measures
of SV during inspiration seen here is evident in the findings of
other investigators, although not always attaining statistical signifi-
cance (Davies et al., 2000; Doerr, Miles,  Frey, 1981; Du Ques-
ney, Stoute,  Hughson, 1987; L. Wang, Patterson,  Raza, 1991).
The causes are unclear. Although interpretations of the source of
the ICG signal tend to emphasize flow in the aortic column (Bern-
stein, 2010; Kubicek, 1989; Sherwood et al., 1990), the possible,
and perhaps large, contribution of cardiodynamics in the pulmo-
nary circuit has also been cited (Denniston et al., 1976; Miles 
Gotshall, 1989; Noordegraaf et al., 1998; Patterson, 1985; Saito,
Goto, Terasaki, Hayashida,  Morioka, 1983; Wang  Patterson,
1995). In any case, these findings point to significant complications
in any attempt to use the ICG method to evaluate cardiorespiratory
interactions.
The BCG signal appeared to be similarly affected by phase of
respiration, insofar as the major component (I-J wave) was signifi-
cantly larger and earlier during inspiration. This finding is in accord
with those of other investigators (Dock, 1962; Starr  Friedland,
1946; Starr et al., 1939; Williams  Gropper, 1951), using tradi-
tional BCG recording techniques, who have interpreted them as
showing that the BCG signal can be ascribed at least in part to right
ventricular ejection that is increased in volume and with shortened
PEP during inspiration (Brecher  Hubay, 1955; Gabe et al., 1969;
Kim et al., 1987; Lauson et al., 1946; Leighton et al., 1971).
The measures related to vascular dynamics present interpretive
challenges. PTT, measured variously using conventional measures
as well as in terms of the transit time between two LDV pulses
Table 5. Mean Values and Effects of Phase of Respiration Cycle for Arterial Timing Measures
Vascular timing n
Mean
(raw units)
IA Prop
[ 6 CI]
IP Prop
[ 6 CI]
EA Prop
[ 6 CI]
EP Prop
[ 6 CI]
EP minus IP
[ 6 CI]
PTT – LDV Bpnt
to ear lobe
32 82.3 ms 1.013
[1.006, 1.020]
1.036
[1.024, 1.049]
0.972
[0.964, 0.980]
0.979
[0.970, 0.988]
20.057
[20.078, 20.037]
PTT – LDV Bpnt
to radial artery
32 102.9 ms 1.006
[1.002, 1.011]
1.030
[1.020, 1.039]
0.985
[0.981, 0.989]
0.979
[0.972, 0.986]
20.051
[20.0667, 20.035]
PTT – LDV Bpnt to
dorsalis pedis artery
25 175.3 ms 1.005
[1.001, 1.008]
1.014
[1.007, 1.021]
0.991
[0.986, 0.996]
0.990
[0.986, 0.995]
20.024
[20.035, 20.013]
PTT – Proximal to
distal LDV
32 6.6 ms 1.019
[0.994, 1.044]
1.068
[1.010, 1.126]
0.976
[0.957, 0.995]
0.937
[0.891, 0.982]
20.132
[20.234, 20.029]
Midsystolic reflection
latency
32 122.6 ms 0.997
[0.992, 1.002]
0.999
[0.989, 1.008]
0.996
[0.993, 0.999]
1.008
[0.999, 1.017]
0.009
[20.009, 0.028]
Late systolic reflection
latency
32 228.8 ms 0.999
[0.996, 1.003]
0.980
[0.974, 0.987]
1.010
[1.006, 1.014]
1.010
[1.006, 1.014]
0.029
[0.020, 0.039]
Note. Shown in Column 3 are the mean values, averaged over all four phases. The values in Columns 4–7 are expressed as proportions of the mean
values, for the inspiration active (IA), inspiration peak (IP), expiration active (EA), and expiration peak (EP) phases, and associated 95% CIs. The val-
ues in the right column are differences between EP and IP, and associated CIs, which in no cases except midsystolic reflection latency includes zero
(indicated in italicized text).
12 E.J. Sirevaag et al.
along the carotid, agreed in showing longer times (i.e., slower
PWV) during inspiration. As computed for the foot, radial, and ear
measures (all with respect to the B point of the LDV carotid pulse),
a possible contribution from PEP was eliminated. While it is possi-
ble that these effects derive from changes in vascular tone over the
respiration cycle, an interpretation in terms of macrocirculatory
factors is also plausible. The direct relationship between PWV and
BP is well established (Weltman et al., 1964), and on this basis the
slower PWV during inspiration could be explained in terms of the
concomitant lowered BP. Arterial stiffness is increased at higher
BPs (Cunha, Benetos, Laurent, Safar,  Asmar, 1995; Laurent et al.,
1994) even over the course of the blood pressure cycle (J. K.-J. Li,
Cui,  Drzewiecki, 1990). The possibility that HR may contribute
to the changes in PWV (as well as features of the pulse contour) can
also be considered. Although the observed effects and associated
interpretation have been variable and depend on measurement site
(e.g., Wilkinson et al., 2000), prior evidence from pacing (Haesler,
Lyon, Pruvot, Kappenberger,  Hayoz, 2004; Liang et al., 1999;
S. C. Millasseau, Stewart, Patel, Redwood,  Chowienczyk, 2005)
and cross-sectional studies (Sa Cunha et al., 1997) have pointed to a
direct relationship between HR and PWV—findings that are oppo-
site to the relationship observed here (i.e., decreased PWV but
increased HR during inspiration).
The passive nature of these changes in PWV is further sup-
ported by our findings of parallel changes along the 4-cm distance
separating the two LDV targeting sites on the carotid. It was
observed that the local PWV of 6 m/s was reduced by about 12%
during the inspiration phase, in comparison to expiration. Given
that the carotid in humans is almost solely an elastic vessel, the
stiffness of which is regulated by the respective engagement of
elastin and collagen fibers rather than muscular function (Bonyhay
et al., 1997), and questionable involvement of endothelial factors
(Horvath, Pinter,  Kollai, 2012), it is unclear how the rapid
within-cycle respiration changes in carotid PWV could be mediated
by some active process. From a technical perspective, it is worth
noting that carotid PWV is the subject of intense interest because
of promising clinical applications (Konofagou, Lee, Luo, Provost,
 Vappou, 2011), and thus the ability to measure it from the LDV
signal may be of significant value. A dedicated multiple beam
LDV system for measuring carotid PWV has been described
(Campo  Dirckx, 2011). Additional measures of timing were
derived here from mid- and late systolic contour features of the dis-
tal LDV carotid pulse, with the intent of investigating the possibil-
ity that the latencies could serve as measures of vascular RTT of
the pressure wave to and from an equivalent reflection site (Qasem
 Avolio, 2008). Although there have been some reports of a close
relationship between PWV and RTT (Qasem  Avolio, 2008; B.
E. Westerhof et al., 2006), these findings and their interpretation
remain controversial (Baksi et al., 2009; Gurovich, Beck,  Braith,
2009; J. G. Kips et al., 2009; B. E. Westerhof, van den Wijngaard,
Murgo,  Westerhof, 2008). The analysis here did not produce evi-
dence for the expected relationship between RTT and PWV.
Whereas the midsystolic peak was unaffected, the late systolic
peak paradoxically was decreased in latency during the inspiration
phase, that is, in the opposite direction from other vascular timing
measures. An important qualification is that the analysis here can-
not be generalized to other more conventional implementations of
RTT, which are based on detection of a midsystolic inflection point
taken as the earliest arrival of the major reflection wave.
There are related questions of interpretation that apply to the
measure of augmentation ratio, which was operationalized here in
terms of the ratio of amplitudes of the separated backward and for-
ward waves. This ratio is conceptually similar to the AIx, which is
usually measured in terms of the amplitudes late and early in sys-
tole. AIx is determined by the relative amplitude of the reflected
wave, which in turn is thought to be determined by the level of
impedance encountered peripherally (and perhaps the speed with
which the reflected wave returns; Nichols  Singh, 2002). Consist-
ent with this interpretation, the magnitude of the reflection wave
that contributes to AIx is commonly found to increase with aging
as well as hypertension and other clinical end points (Nichols 
Singh, 2002), and also to show acute increases upon noradrenergic
stimulation including the cold pressor test (Casey, Braith,  Pierce,
2008; Liu et al., 2011). Overall, the augmentation ratios observed
here were small, consistent with expectations when recording from
young healthy individuals (Nichols et al., 2011) in the seated rather
than supine body position (van den Bogaard et al., 2011; Vrachatis
et al., 2014). Nevertheless, the augmentation ratio showed signifi-
cant effects of respiration cycle. It was found to be larger during
the expiration phase than during inspiration—an effect that, if taken
at face value, would suggest that systemic resistance increased dur-
ing expiration. As noted above, the ICG-based cardiac output
measurements were not adequate to support computation of sys-
temic resistance; a substitute analysis utilizing cardiac output based
on the LDV SV found that the attendant “systemic resistance” was
increased marginally (about 2%) during EP compared to inspira-
tion. This difference was not significant at the time of IP (EP minus
IP, 6 CI) 5 0.018 (-0.010, 0.047), although was significant if the
comparison was based on IA (EP minus IA, 6 CI) 5 0.026 (0.009,
0.0440).
The relevant evidence regarding this appearance of dynamic
vascular adjustment over the respiration cycle is equivocal. Studies
of sympathetic nerve traffic have found evidence for respiration-
related rhythmicity in sympathetic drive to the cutaneous, muscu-
lar, and splanchnic vascular beds (Cogliati, Magatelli, Montano,
Narkiewicz,  Somers, 2000; Eckberg, Nerhed,  Wallin, 1985;
Malpas, 1998; Pilowsky, 1995), which in turn may derive from
central and reflex mechanisms including pulmonary stretch reflexes
(Looga, 1997) and baroreceptor reflexes (G. G. Wallin  Charkou-
dian, 2007). These findings have led some investigators to accept
the existence of respiration-related changes in resistance; Limberg
et al. (Limberg, Morgan, Schrage,  Dempsey, 2013), for example,
cite evidence leading them to conclude that “powerful within-
breath respiratory modulation of sympathetic vasoconstrictor activ-
ity has been well documented in humans and experimental ani-
mals” (p. H1615). The effects at the vasomotor level have also,
however, been shown to depend strongly on multiple factors
including species differences, specific vascular bed, and ventilation
parameters, and to show complex phase relationships with the res-
piration cycle. Of particular importance is respiration rate, with the
vasomotor effects being most prominent at slow rates (Seals,
Suwarno,  Dempsey, 1990; Stauss, Anderson, Haynes,  Kregel,
1998). This is in accord with the broad indications of general slug-
gishness of the sympathetic vasomotor signal transduction effects,
which include multisecond delays observed in the sympathetic
response to abrupt stimulation (Toska, Eriksen,  Walloe, 1994; B.
G. Wallin  Eckberg, 1982), and the effective low-pass filter on
rhythmic activities with a corner frequency estimated to be in the
range of 0.1 to 0.2 Hz (Bernardi et al., 1997; Julien, Malpas, 
Stauss, 2001; Rosenbaum  Race, 1968; Saul et al., 1991). On bal-
ance, our findings would admit the possibility of some vascular
resistance effects, probably more evident in the slow-breathing par-
ticipants in this study (with the slowest at 0.12 Hz), but perhaps
Laser Doppler vibrometry for cardiovascular assessment 13
even in some form (albeit attenuated) in the faster-breathing
individuals.
Measurement Issues
Recording the carotid pulse. As cited above, the usefulness of
the carotid pulse lies in its close similarity to the central (aortic) BP
pulse, which is valuable because of the information it conveys
regarding myocardial performance, afterload, vascular compliance,
and diastolic function (Nichols et al., 2011). The external carotid
pulse has been the subject of an extremely large clinical and scien-
tific literature (Nichols et al., 2011; Tavel, 1972), and has been suc-
cessfully utilized in large-scale studies of clinical end points (e.g.,
Rietzschel et al., 2007). The carotid measurement site has the addi-
tional advantages of its intimate relevance for cerebral circulation
and carotid baroreceptor function (Steinback, O’Leary, Wang, 
Shoemaker, 2004). There are, however, limiting factors in practice.
Recording the carotid pulse using tonometric, ultrasound, photo-
electric, and mechanical sensors is described as raising issues
regarding operator training, repeatability, patient comfort, hold-
down pressure, and the small but nonnegligible risk of disrupting
plaque (J. Kips et al., 2010; S. Millasseau  Agnoletti, 2015;
O’Rourke, Pauca,  Jiang, 2001). For these reasons, the more com-
mon method is to estimate the central BP on the basis of a transfer
function applied to the pulse recorded from some peripheral artery
(usually radial, where the underlying bony support facilitates flat-
tening of the artery for tonometric recording). The various elements
surrounding use of the transfer function have been the subject of
debate.
Of greater concern when using the carotid site, especially to
psychophysiologists, are the possible autonomic consequences
associated with external massage and pressure applied to the
carotid baroreceptors—strong enough that carotid massage has tra-
ditionally been an option for bedside testing of autonomic failure
(Schweitzer  Techholz, 1985). In this critical regard, the LDV
method, in which there is the complete elimination of any contact,
would appear to offer an advantage. It is important to frame this
with the recognition that the diameter changes that form the bases
of the LDV carotid pulse signal, while closely related to BP, are
not equivalent. The differences arise from nonlinearities in the
carotid pressure-distension curve, following the deviations from
pure elasticity produced by the respective engagement of elastin
and collagen fibers as diameter changes (London  Pannier, 2010;
Meinders  Hoeks, 2004; S. Millasseau  Agnoletti, 2015). It is
also important to note that the carotid LDV pulse at the skin is
uncalibrated with respect to absolute diastolic and systolic pressure
values, since it will be affected by such factors as distensibility of
the carotid artery, intervening subcutaneous tissue, precision of
laser targeting, and small in-plane changes in position during the
recording period. The signal could in principle be calibrated with
respect to pressure at a brachial or radial site (Agnoletti et al.,
2012; Vermeersch et al., 2008), although calibrating with respect to
noninvasive BP measures is itself a matter of debate (Adji 
O’Rourke, 2012; Hope, Meredith,  Cameron, 2004). Even when
interpreted as an uncalibrated pressure pulse waveform, however, a
considerable amount of information relating to such variables as
cardiac output and vascular dynamics can be adduced—especially
with respect to changes in the measures on an intraindividual basis.
LDV metrological considerations. The extent of agreement
between signals obtained using the LDV method and other nonin-
vasive methods remains to be determined, but some general consid-
erations can be offered. A fairly large number of commercial
noninvasive methods for measuring central BP have been described
(S. Millasseau  Agnoletti, 2015; Narayan et al., 2014). The
method dependency of the derived measures is widely acknowl-
edged—leading one editorial commentator to cite the “likelihood
that as many different estimated . . . central BP parameters can be
obtained as there are devices designed to obtain them” (Cameron,
2013, p. 27). A major consideration pertains to the frequency
response, which is generally higher in the intraarterial BP signal
than is accurately represented in the tonometrically derived transfer
functions for estimating central BP (Laurent et al., 2006). As
another example, BP variability measured with the Finapres
method has been found to underestimate variability in the high fre-
quency respiration band, while overestimating it at lower frequen-
cies in comparison to the intraarterial pressure signal (Kornet,
Hoeks, Janssen, Willigers,  Reneman, 2002).
A noteworthy feature of the LDV method in general is the
broadband frequency response, capable of transducing mechanical
energy (in the case of the Polytec IVS-300) through 22 kHz. This
compares with conventional tonometric sensors, which are limited
in use to an effective upper frequency of about 5 Hz, despite nomi-
nally greater capability in the sensor itself (Matthys  Verdonck,
2002; Sato, Nishinaga, Kawamoto, Ozowa,  Takatsuji, 1993).
Perhaps consistent with the broad frequency response is the high
degree of texture observed here in the LDV carotid pulse wave-
form. At least three systolic peaks (the forward wave and two in
the backward wave) were reliably measured, but typically there
were additional inflections and peaks that were readily apparent. It
is possible that some of these peaks might reflect mechanical activ-
ity referred from more distal sites—perhaps including gross BCG
influences, although it should be noted that none of the major BCG
components (see Figure 5), if adjusted for the time between R
wave and LDV carotid B point, could be readily mapped onto the
LDV carotid pulse waveform. Other investigators, using different
measurement techniques, have also described the presence of mul-
tiple reflection and rereflection waves in the central BP pulse
(Baruch, Kalantari, Gerdt,  Adkins, 2014; Berger, Li, Laskey, 
Noordbergraaf, 1993; Wang, Xu, Feng, Meng,  Wang, 2013).
There are also higher-frequency signals present in the LDV sig-
nal, albeit usually of much lower amplitude. Time-frequency repre-
sentations of the LDV carotid pulse invariably show time-locked
energy at frequencies exceeding 100 Hz. The energy includes local
vessel sounds (Hasegawa, Rodbard,  Kinoshita, 1991), used here
as an initial basis for detection of candidate pulses. These sounds
can appear in the form of bruits in the presence of atherosclerosis
of the carotid (Pickett, Jackson, Hemann,  Atwood, 2008). Also
possibly apparent are even higher sounds including poststenotic
wall vibrations exceeding 2 kHz—the study of which appears to be
among the earliest examples of using the LDV for a biological sens-
ing application (Stehbens, Liepsch, Poll,  Erhardt, 1995). Because
of the high acoustic impedance mismatch between skin and air, little
if any vibration activity from ambient sounds can be impressed on
the skin to interfere with these measures (Katz, 2000).
Relevance for psychophysiological studies. These findings add
to the growing body of evidence regarding the range of physiologi-
cal signals, all of interest to psychophysiology, that can be recorded
using the LDV method (and kindred methods based on self-mixing
interferometry and laser speckle tracking). In keeping with the gen-
eral observation that physiology (particularly at the system level)
typically includes an appreciable mechanical component, these
applications have demonstrated effectiveness in a number of
14 E.J. Sirevaag et al.
systems including respiration (Marchionni, Scalise, Ercoli,  Tom-
asini, 2013; Scalise, Ercoli,  Marchionni, 2010; Scalise, Ercoli,
Marchionni,  Tomasini, 2011; Scalise, Marchionni,  Ercoli,
2010), speech (Avargel  Cohen, 2011), mechanical myogram
(Rohrbaugh, Sirevaag,  Richter, 2013; Scalise, Casaccia, March-
ionni, Ercoli,  Tomasini, 2013), and biomechanics (P. Castellini
 Tomasini, 1998; Nataletti, Paone,  Scalise, 2005; Revel, Scal-
ise,  Scalise, 2003; Scalise, Rossetti,  Paone, 2007; Valentino
et al., 2004), in addition to the CV system. CV variables include
the phonocardiogram (De Melis, Morbiducci,  Scalise, 2007),
PTT (De Melis et al., 2008), heart rate (Marchionni et al., 2013;
Morbiducci, Scalise, De Melis,  Grigioni, 2007; Scalise  Morbid-
ucci, 2008), movements of the precordium (Hong  Fox, 1997;
Scalise, Morbiducci,  De Melis, 2006; Schuurman, Rixen, Swenne,
 Hinnen, 2013), and the BP pulse (Campo, Segers,  Dirckx,
2011; Capelli, Bollati,  Giuliani, 2011; Desjardins, Antontelli, 
Soares, 2007; Hong  Fox, 1994, 1997; Y. Li, Segers, Dirckx, 
Baets, 2013; Pinotti, Paone, Santos,  Tomasini, 1998).
An attractive feature of all of these applications for psychophy-
siological purposes lies in the noncontact nature of the method,
which supports testing in the absence of attached sensors, or rigid
requirements for body position (although active motion including
speech and orofacial movement poses problems for currently avail-
able methods). The study reported here entailed preparation of the
skin as well as the physical attachment of additional sensors, but
these encumbrances are not required by, or inherent in, the LDV
method if used on a stand-alone basis. LDV systems are available
in a range of configurations, providing beam steering, scanning,
multiaxial (3D), and autofocus capabilities. Scanning systems pro-
vide extensive capabilities for data analysis in time, frequency, and
spatial domains. The data presented here are based primarily on a
Polytec single-point industrial vibrometer (IVS 300). Detailed
specifications for this and other systems are available at the Polytec
website (http://www.polytec.com/us/). The IVS-300 system pro-
vides an analog velocity output signal, which in the case of this
study was processed using custom algorithms as described above.
Psychophysiological applications are facilitated by newly available
LDV systems based on invisible infrared lasers that are completely
eye safe (Class 1; Dr€abenstedt, Sauer,  Rembe, 2012), coupled
with computer vision-guided methods for automated targeting and
dynamic tracking of minimally constrained individuals and proc-
essing algorithms that are effective with signals from untreated
skin. The method can be made to work in harsh environments,
including the MR scanner where the laser head can be distanced
from the magnetic field. Perhaps the most significant implications
refer to the character of the physiological data obtained. The self-
awareness that arises with being monitored, the physical encum-
brance associated with some types of sensors, and the social con-
text (including interpersonal touch) inherent in the typical
laboratory environment are often recognized as important ingre-
dients—ones that could be made accessible for reevaluation using
noncontact methods in this class.
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Laser Doppler vibrometry for cardiovascular assessment 15
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  • 1. Cardiorespiratory interactions: Noncontact assessment using laser Doppler vibrometry ERIK J. SIREVAAG,a SARA CASACCIA,b,c EDWARD A. RICHTER,b JOSEPH A. O’SULLIVAN,b LORENZO SCALISE,c AND JOHN W. ROHRBAUGHa a Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA b Preston M. Green Department of Electrical and Systems Engineering, School of Engineering, Washington University in St. Louis, St. Louis, Missouri, USA c Department of Industrial Engineering and Mathematical Science, Universita Politecnica delle Marche, Ancona, Italy Abstract The application of a noncontact physiological recording technique, based on the method of laser Doppler vibrometry (LDV), is described. The effectiveness of the LDV method as a physiological recording modality lies in the ability to detect very small movements of the skin, associated with internal mechanophysiological activities. The method is validated for a range of cardiovascular variables, extracted from the contour of the carotid pulse waveform as a function of phase of the respiration cycle. Data were obtained from 32 young healthy participants, while resting and breathing spontaneously. Individual beats were assigned to four segments, corresponding with inspiration and expiration peaks and transitional periods. Measures relating to cardiac and vascular dynamics are shown to agree with the pattern of effects seen in the substantial body of literature based on human and animal experiments, and with selected signals recorded simultaneously with conventional sensors. These effects include changes in heart rate, systolic time intervals, and stroke volume. There was also some evidence for vascular adjustments over the respiration cycle. The effectiveness of custom algorithmic approaches for extracting the key signal features was confirmed. The advantages of the LDV method are discussed in terms of the metrological properties and utility in psychophysiological research. Although used here within a suite of conventional sensors and electrodes, the LDV method can be used on a stand-alone, noncontact basis, with no requirement for skin preparation, and can be used in harsh environments including the MR scanner. Descriptors: Cardiovascular, Respiration, Other, Autonomic The goal of this report is to describe in detail an application of a noncontact method for measuring mechanophysiological activity based on the technique of laser Doppler vibrometry (LDV). We include a brief discussion of the underlying theoretical and applica- tion principles, including methods for LDV signal acquisition and analysis. We present findings that testify to the effectiveness of the LDV method for the study of cardiorespiratory signals, validated within a broad context of convergent information derived from conventional sensors that are commonly used in psychophysiologi- cal research. The focus in this report is on changes in cardiovascu- lar (CV) activity over the respiratory cycle, an issue of abiding importance for psychophysiologists. Spontaneous breathing, while resting, provides a procedurally simple and well-characterized assessment opportunity, although this apparent simplicity belies a broad and richly textured pattern of effects. Our presentation emphasizes the breadth of these effects over the respiratory cycle, using multiple LDV as well as conventional measures, and illus- trates the capabilities of the LDV as a method for obtaining advanced measures—on a completely noncontact and unobtrusive basis. LDV as a Physiological Recording Method LDV is widely used in engineering and industrial settings for non- destructive testing of mechanical vibration (Castellini, Martarelli, Tomasini, 2006). LDV is based on the detection of the Doppler shift in scattered light (in comparison to an internal reference), as the laser beam is directed at a moving surface. Commercial off-the- shelf instruments are available in a number of configurations, uti- lizing several different laser wavelengths (including infrared), scan- ning capabilities, and optics that support applications ranging from microscopy to long-range recordings at distances of at least several hundred meters. A noteworthy feature of the method is the metro- logical quality, which supports detection of displacements in the pm range, with a band-pass extending from DC up to vibrations in the 20–50 kHz range (and well beyond in some systems). Since the recording basis is noncontact, some of the common problems asso- ciated with other sensor types (e.g., mass loading, use of adhesives, intrinsic resonance, cable artifacts) are avoided. As discussed Address correspondence to: John W. Rohrbaugh, Department of Psy- chiatry, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, USA. E-mail: jwrohrba@wustl.edu 1 Psychophysiology, 00 (2016), 00–00. Wiley Periodicals, Inc. Printed in the USA. Copyright VC 2016 Society for Psychophysiological Research DOI: 10.1111/psyp.12638
  • 2. below, the noncontact basis is of special salience for psychophysio- logical applications. The usefulness of LDV as a physiological recording method lies in the observation that most physiological activities (particu- larly at the system level) include mechanical energy that is trans- mitted to the skin. It follows that activities in a range of physiological response systems can be characterized in terms of their mechanical representation, especially in the CV system (see Discussion). Conventionally, mechanocardiovascular activity is detected by palpation or auscultation, where it forms a critical ele- ment in the clinical exam (Perloff, 2000). There have been many methods described over the years for sensing and objectively quan- tifying such activity, utilizing a range of pressure, microphone, strain, accelerometry, displacement, proximity, ultrasound, and other sensors (e.g., Eddleman, 1974; Forrester, Vas, Diamond, Silverberg, 1978; Starr, Rawson, Schroeder, Joseph, 1939; Tavel, 1972). Specific considerations apply to all of these methods, with respect to factors such as accuracy and susceptibility to noise, and especially patient constraint and comfort—which in some cases would appear to limit their utility for psychophysiological studies of behaviorally active individuals. Despite such measure- ment issues, these studies have left a richly detailed literature regarding the properties of the mechanophysiological activities, which can guide the interpretation of the LDV signals. LDV Recording of the Carotid Pulse As described in voluminous literatures, the mechanophysiological signs of CV activity are widely distributed over the body, reflecting variously multivectorial ballistic activity, movements of the precor- dial surface including heart sounds, and more focal activities over superficial blood vessels. A substantial scientific and clinical litera- ture has dwelled with the external carotid pulse (Nichols, O’Rourke, Vlachopoulos, 2011; Tavel, 1972), which is the prin- cipal focus of the research described here. The pulse appears to arise from changes in lumen diameter associated with variations in the internal blood pressure (BP). Since the carotid is a distensible vessel, the diameter is closely related to internal pressure over the physiological range (Sugawara, Niki, Furuhata, Ohnishi, Suzuki, 2000)—albeit with some nonlinearity deriving from the visco- elastic properties of the arterial wall (Bonyhay, Jokkel, Karlocai, Reneman, Kollai, 1997; Kamenskiy et al., 2012). To the extent that these wall movements are transmitted to the overlying skin, the external carotid pulse signal can be taken as a close (although not identical) surrogate of the central BP pulse. The waveform shows a high degree of similarity with that of the ascending aortic pulse (with only minor differences; C.-H. Chen et al., 1996; Kelly, Karamanoglu, Gibbs, Avolio, O’Rourke, 1989; Sztrymf, Jacobs, Chemla, Richard, Millasseau, 2013; Van Bortel et al., 2001). As such, the carotid pulse waveform harbors a considerable amount of information regarding myocardial and vascular dynamics. We have shown that the texture of the carotid pulse waveform (recorded by LDV) is indeed sufficiently rich to support biometric recognition (M. Chen et al., 2010; Odinaka, O’Sullivan, Sirevaag, Rohr- baugh, 2015). More than a century of clinical research has identi- fied in the carotid pulse multiple signs of CV disease (Nichols et al., 2011; Perloff, 2000; Tavel, 1972). Of special importance here is the information that is directly relevant to CV psychophysi- ology—including heart rate, systolic time intervals, cardiac output, arterial compliance, coronary perfusion, and impedance at the microvasculature level (Nichols et al., 2011; N. Westerhof, Stergio- pulos, Noble, 2010). There are precedents for using the carotid pulse in psychophy- siological research. These include a series of studies by Obrist et al., who proposed that the velocity representation (i.e., peak slope of the upstroke, dP/dt) of the carotid pulse could serve as a measure of extrinsic b-adrenergic influences on the myocardium. The slope measure was observed to increase in such tasks as shock avoidance and nonsignaled reaction time, and the effect was abol- ished by the administration of the b-adrenergic blocking agent pro- pranolol (Obrist et al., 1972, 1974). Questions were raised about the quality of the dP/dt measure, with respect to the role of non- neurogenic factors and its purity as a measure of b-adrenergic effects, as well as methodological issues including the absence of calibration (Heslegrave Furedy, 1980; Light Obrist, 1980; Obrist Light, 1980). The recording technique was cumbersome, involving (a) attachment of a low-frequency microphone over the carotid using an adhesive collar, (b) covering the microphone with foam rubber to suppress extraneous noise, (c) holding the assembly in place with an elastic bandage, and (d) using a neck collar to sta- bilize the head and neck. Even at that, motion artifact proved to be a limiting problem. An improved recording method involving fur- ther stabilization of the head using a rigid frame has been described by Ifuku et al. (Ifuku, Moriyama, Arai, Shiraishi-Hichiwa, 2007; Ifuku, Taniguchi, Matsumoto, 1993, 1994; Moriyama Ifuku, 2007, 2010) (and used in a series of studies involving cold pressor, exercise, and other manipulations to validate the carotid dP/dt pulse as a measure of inotropic influences), but in general the meth- ods are not compatible with recording in behaviorally uncon- strained conditions. More recently, hand-held or mounted tonometric devices have been applied for this purpose (e.g., C.-H. Chen et al., 1996; Edwards, Roy, Prasad, 2008; Geleris, Stavrati, Boudoulas, 2004; LaFleche, Pannier, Lalaux, Safar, 1998), but shared limitations can be identified for all of these methods, including the requirements for rigid assumption of a given postural position as well as corruption of the signals of interest produced by mechanical stimulation of the carotid baroreceptors (see Discus- sion). As we show here, the noncontact LDV method may offer significant advantages with respect to these issues. The Range of Cardiorespiratory Interactions: Brief Overview The importance of respiration variables in psychophysiological research is well established (Boiten, Frijda, Wientjes, 1994; Grossman Taylor, 2007; Wientjes, 1992), as are the substantial interactions among respiration and CV variables (see below). The interactions take several forms, including direct effects of respira- tion. These interactions are of sufficient clarity and robustness to prompt frequent suggestions that they can be harvested for pur- poses of monitoring ventilation parameters (Ahlstrom, Johansson, Lanne, Ask, 2004; Chua Heneghan, 2006; De Meersman et al., 1996; Johansson Oberg, 1999; Lazaro, Gil, Bailon, Laguna, 2011; Nilsson, Johnsson, Kalman, 2001; Orini, Pelaez- Coca, Bailon, Gil, 2011; Pitson, Sandell, van den Hout, Stra- dling, 1995). It follows that a full understanding of instantaneous CV activity would require detailed appreciation of local respiratory variables (M. T. Allen, Sherwood, Obrist, 1986; Grossman, 1983). It also follows that many common situations that affect the pattern of breathing (e.g., speaking, Beda, Jandre, Phillips, Giannella-Neto, Simpson, 2007; Winkworth, Davis, Adams, Ellis, 1995) as well as physical activity, skilled performance, anxi- ety, and sleep will show respiration-dependent CV effects. 2 E.J. Sirevaag et al.
  • 3. Cardiorespiratory interactions over the breathing cycle have been reviewed extensively elsewhere (De Burgh Daly, 1986; Feihl Broccard, 2009a; Olsen, Tyson, Maier, Davis, Rankin, 1985; Robotham, 1988; Wise, Robotham, Summer, 1981). The most familiar form of such interactions in the psychophysiology litera- ture is almost certainly respiratory sinus arrhythmia (RSA), whereby interbeat interval (IBI) is observed to shorten during the inspiration phase. Although RSA appears to be complexly deter- mined, there is clear evidence for close association with respiration (Brown, Beightol, Koh, Eckberg, 1993; Denver, Reed, Porges, 2007; Grossman Taylor, 2007). There is a rich body of evidence attesting to links between RSA and a range of variables including development, affect, performance, and social cognition as well as individual differences and clinical status (J. J. B. Allen, Chambers, Towers, 2007; Porges, 2007). It has been suggested that a way of understanding RSA, as well as the much broader constellation of respiration-related CV effects, is to consider them from a stand- point of gas exchange efficiency, whereby oxygen and carbon dioxide transfer is optimized by providing for greater volume and exchange rate of blood in the pulmonary circuit during inhalation (Galletly Larsen, 1998; Giardino, Glenny, Borson, Chan, 2003; Hayano Yasuma, 2003; Hayano, Yasuma, Okada, Mukal, Fujinami, 1996; Yasuma Hayano, 2004). Without regard to the accuracy or completeness of this overview from a “teleological” point of view (Tzeng, Sin, Galletly, 2009), it does provide a useful conceptual framework for categorizing the various effects seen under conditions of normal spontaneous breathing. This pulmonary vasocongestion during inspiration is key to under- standing the pattern of hemodynamic effects. The displacement of blood toward the pulmonary circuit appears to derive primarily from enhanced venous return prompted by the fall in intrathoracic pressure, and corresponding increases in right ventricular stroke volume (SV) and ejection duration. The pooling of blood in the lungs is facilitated by the high capacitance, low resistance, and comparatively low pressure in the pulmonary arterial system (Murgo Westerhof, 1984; Rigolin, Robiolio, Wilson, Harrison, Bashore, 1995). This relative displacement of blood during inspiration to the pulmonary circuit has readily identifiable consequences in the sys- temic circulation. Many of the effects can be traced to the reduction in left ventricular SV (Andersen Vik-Mo, 1984; Brenner Waugh, 1978; Cahoon, Michael, Johnson, 1941; Caiani et al., 2002; Davies et al., 2000; Gabe et al., 1969; Guz, Innes, Murphy, 1987; Karam, Wise, Natarajan, Permutt, Wagner, 1984; Karlocai, Jokkel, Kollai, 1998; Lauson, Bloomfield, Cournand, 1946; Robotham et al., 1978; Robotham Mitzner, 1979; Robotham, Rabson, Permutt, Bromberger-Barnea, 1979; Ruskin, Bache, Rebert, Greenfield, 1973; Toska Eriksen, 1993)—a primary cause of which appears to be the reduction in left ventricular diastolic filling (reduced preload). Other possible mechanisms have been cited, including reduction in left ventricular volume (associated with leftward bulge of the septum produced by increased right ventricle filling), altered phase relationships of the return from the lungs, and increase in left ventricular afterload caused by negative pleural pressure and associated impediment on ventricular ejection (Karam et al., 1984; Robotham, 1988; Robotham Mitzner, 1979). Reduced diastolic filling time associated with the higher heart rate is another possible contributor (Toska Eriksen, 1993), although relationships among hemodynamic variables and RSA are complex (Elstad, Toska, Chon, Raeder, Cohen, 2001; Parati et al., 1987; Taylor Eckberg, 1996). The magnitudes of the SV effects are substantial. A detailed radionuclide ventriculography study (Kim et al., 1987), for example, reported that left ventricular stroke volume decreased during inspiration by 176 7%, in contrast to an increase in right ventricular SV of 226 18%. The decrease in left ventricular filling is also associated with a lengthening of pre- ejection period (PEP) and decrease in left ventricular ejection time (LVET; Johansson, Ahlstrom, Lanne, Ask, 2006; Nandi, Pigott, Spodick, 1973; van Leeuwen Kuemmell, 1987). Whereas RSA appears to include a possible role of central oscillators or gating mechanisms (Eckberg, 2003), the systemic hemodynamic effects depend heavily on mechanical factors—as attested to by findings that they are preserved with transplanted or otherwise denervated hearts (Bernardi, Keller et al., 1989; Macor, Fagard, Vanhaecke, Amery, 1994; Zhang et al., 2002). As would be expected given changes in SV of this magnitude, a decrease in systolic blood pressure (SBP) during inspiration is rou- tinely observed (Badra et al., 2001; Davies et al., 2000; Pitzalis et al., 1998; Triedman Saul, 1994). The periodic fluctuations in SBP were correctly ascribed to respiration as long ago as 1828 by Pouiselle (Larsen, Tzeng, Sin, Galletly, 2010), with normal vari- ation in humans ranging up to 10 mm Hg (beyond which the clini- cally significant sign pulsus paradoxus is identified (Feihl Broccard, 2009b). There are also consistent findings of peripheral effects, including an increase in pulse transit time (PTT, or expressed as a decrease in pulse wave velocity, PWV) during inspiration (Constant, Laude, Murat, Elghozi, 1999; Drinnan, Allen, Murray, 2001; Johansson et al., 2006; Pagani et al., 2003; Weltman, Sullivan, Bredon, 1964). The peripheral pulse amplitude is also affected. Measured photoplethysmographically or using volume and flow- metry methods, the peripheral pulse shows a reduction in pulse amplitude during inspiration under normal breathing rates (Bernardi et al., 1997; Bernardi, Rossi et al., 1989; Johansson Oberg, 1999; Martin, Marcellus, Sykowski, Albany, 1938). As considered in greater detail in the Discussion, there remains some uncertainty regarding the extent to which these and other effects measured peripherally (including possible rhythmicity in systemic vascu- lar resistance) derive from macrocirculatory as opposed to local vascular factors. In the present study, the range of CV effects associated with the respiratory cycle are exploited for purposes of validating the LDV measures of CV function. Our effort is guided by the overall hypothesis that the noncontact LDV method can be used to mea- sure multiple, complex cardiovascular effects—related in this case to the phase of the respiratory cycle as described above variously for chronotropic, inotropic, and vascular effects. We demonstrate how these measures are made accessible for study in minimally constrained, behaviorally active situations—ones free from the bur- den of invasive and obtrusive instrumentation or imposition of a specific posture. The LDV measures are validated both within con- text of the substantial existing literature, as well as a battery of con- vergent measures obtained using conventional psychophysiological methods. Our focus, again, is on the carotid pulse, measured using the LDV method. To our knowledge, there have been few attempts to study the respiration-related changes in the human carotid pulse waveform, and those have focused on systolic time intervals (Leighton, Weissler, Weinstein, Wooley, 1971; Pigott Spo- dick, 1971; van Leeuwen Kuemmell, 1987). Perhaps the most detailed is our prior LDV study, in which a subset of the data uti- lized here were analyzed using a hidden Markov model approach to evaluate waveform features of the LDV carotid pulse (Kaplan, O’Sullivan, Sirevaag, Lai, Rohrbaugh, 2012). It was observed that the state sequences showed periodic, orderly patterns that Laser Doppler vibrometry for cardiovascular assessment 3
  • 4. agreed in most cases with the respiration sequences. Consistent with the effects described above, and evaluated in greater detail here, the states associated with respiration phase differed in several respects. LDV velocity pulses were observed to be of lower ampli- tude during inspiration, and there were effects on systolic time intervals. In the present report, we expand on these observations and provide a more extensive background for interpreting them. Method Recording Protocol Data were obtained during an initial 3-min resting baseline period, as part of a larger experiment that involved a number of subsequent activating maneuvers (not described here). Participants were seated in a padded chair, with a soft foam pad (fashioned from a wheel- chair head support) that loosely cradled the head to help maintain a stable position. Breathing was entirely spontaneous. Participants were instructed simply to sit quietly, but no instructions regarding rate or constancy of breathing pattern were given. Participants Participants were recruited from the general community by adver- tisement, and compensated $30 for participation in a single 2-h ses- sion. All recruitment and testing procedures including the informed consent process were approved by the Washington University Human Research Protection Office. Participants were screened using a telephone interview to be within the age range of 18–30 years, free of any physical disease (including hypertension, asthma, and severe allergies), psychiatric disorder, or acute illness, non- smokers, and not using any medication that might affect cardiores- piratory functioning nor illicit substances. Of the 43 participants enrolled, data from 11 were excluded from further analyses yield- ing a final n 5 32 (19 female, mean age 24 [range 20–29], mean height 172 cm [range 154–191], mean body mass index (BMI) 23.0 [range 18.0–29.0]). Reasons for exclusion were based on strain gauge respiration signal of insufficient quality to support a detailed breath-by-breath analysis (four), poor quality radial BP signal (one), a protocol exception (one), marked irregularities in respira- tion rate including occasional long expiratory pauses (three), and minimal evidence for RSA in the spectral analysis of the electro- cardiogram (ECG)-based heart rate series (two). Physiological Recording LDV recording. LDV data were obtained simultaneously from two sites along the tract of the right carotid, which were identified from visible landmarks. The approach was 458 to the right (approx- imately radial to the neck skin surface at the carotid sites). The sites were separated by 4 cm, with the upper site at the approximate level of the carotid bifurcation. The bottom site was generally more medial, consistent with our systematic mapping studies of the dis- tribution of pulse amplitude along the carotid. The sites were marked by applying an adhesive tape template, with 1-cm open cir- cular patches (center to center separation5 4 cm). The exposed skin at these patches was marked with a thin coating of titanium dioxide, and dusted with retroreflective glass beads (45–63lm diameter). The tape template was removed prior to data acquisition, leaving the two treated skin patches. (Our current analysis capabil- ities are effective with nontreated skin.) The lasers were targeted at the centers of these two patches. Data from the top site were obtained from a Polytec IVS-300 industrial vibrometer (band-pass 0.5Hz to 5 kHz, sensitivity5 5 mm/s/V), and from the lower site with a colinear Polytec PSV-400 scanning vibrometer (band-pass 0 to 5 Khz, sensitivity5 10 mm/s/V). The laser standoffs were at dis- tances corresponding to laser coherence maxima: 92.5 cm (IVS- 300) and 91.5 cm (PSV-400). The latency of the signal from the IVS-300 was adjusted to compensate for the 1.1-ms output delay. Data were sampled at a rate of 12.5 kHz using a Biopac MP150 system, with additional physiological and technical channels (see below) integrated in a common file. Signals were variously down- sampled and processed as described below. Additional physiological signals. To serve as a basis for valida- tion and interpretation, several conventional cardiorespiratory sig- nals were obtained. Participants donned a loose scrub top and disposable foam slippers to facilitate attaching these sensors. Respiratory effort was measured using a Biopac SS5B circum- ferential belt, at a lower thoracic level. ECG was recorded from bilateral sites at the lower rib cage, using a Biopac TEL100 amplifier. Impedance cardiogram (ICG) was measured using a Biopac EBI100-C transducer (frequency 50Hz, gain 5 X/V) using spot electrodes placed bilaterally on the neck, and lateral rib cage (with the inner electrodes at the level of the xiphoid process). Continuous BP was measured on a beat-to-beat basis using a Colin 7000 (bundled with the Biopac system as the NIBP100). This instrument measured the BP from the left radial artery on a beat-to-beat basis, utilizing a tonometric principle. Photoplethysmographic pulse was obtained from the left ear lobe, using a Biopac TDD200 transmission-based sensor and PPG100C transducer. Dorsalis pedis pulse was recorded using a piezo film sensor (Measurement Specialties LDT1-028K, 303 13mm) encased in rubber and taped over the palpable pulse site on the left foot, with constant pressure supplied by a thin, slightly com- pressed foam block. Ballistocardiogram (BCG) was recorded from a sheet of piezo film sensor (Pennwalt Kynar T052B/L) that was incorporated into the chair cushion. Phonocardiogram (PCG) was recorded using a Biopac SS17 microphone, which was taped at a site just to the left of the sternum over the third intercostal space. Data Analysis Segmentation by phase of respiration cycle. The analysis strat- egy entailed binning pulses according to the phase in the associated strain gauge respiration effort signal. The respiratory cycle was divided into four algorithmically identified bins, as illustrated for a fragment of a typical respiratory effort signal in Figure 1. The accu- racy of the segmentation algorithm was visually confirmed for all individual breaths. The first step involved identification of the points of inspiration and expiration peaks. The amplitude of each breath was then computed as the difference between successive inspiration peaks (IP) and expiration peaks (EP). The correspond- ing bins were then identified as the areas bounding the peaks where the value of the respiration signal was within 10% of the IP or EP. Periods defined as inspiration active (IA) and expiration active (EA) were then identified as the regions between corresponding transition points. Individual cardiac beats were assigned to these 4 E.J. Sirevaag et al.
  • 5. bins on the basis of the moment of upstroke of the carotid LDV pulse (the B point—see below). Processing of LDV signals. The LDV pulses were analyzed using custom methods developed within a MATLAB (MathWorks) environment. The first stages involved suppressing speckle dropout artifacts (an inherent property in the reflection from a coherent source) and downsampling to 1000 Hz after antialias filtering. The speckle dropout artifact suppression algorithm involved detection of aphysiologically large and abrupt transitions in the signal (from the 2nd derivative of the native velocity signal), removing the affected points and resampling through the gaps in the original velocity signal using linear interpolation. Only on rare occasions was more than one consecutive point affected. The IVS-300 signal was inverse filtered (Elbert Rockstroh, 1980) to reverse the effects of the original 0.5 Hz HP filter in the IVS-300 output (which was modeled as a first-order Butterworth filter). The accuracy of this procedure was confirmed in a test in which the inverse filtered signal was directly compared with a simultaneous PSV-400 signal (band-pass flat to DC) from the same target site. After additional low-pass digital filtering at 150 Hz, the native velocity signal was variously differentiated to assist in landmark identification, and integrated to develop a displacement signal comparable to the pres- sure pulse. Individual LDV pulses were detected and localized independently of the ECG signal (i.e., based solely on features intrinsic to the LDV signal), using an algorithm that focused on detection of high-frequency bursts associated with carotid vessel sounds as well as gross pulse waveform features—then identifying the zero crossing of a tangent line fitted through the ascending peak in the differentiated waveform to identify the moment of the upstroke (labeled here the B point, reflecting the equivalent land- mark in the ICG signal). Individual detections were reviewed and edited as appropriate. Most of the analyses presented below (with exceptions noted) were based on the signal from the distal (IVS- 300) laser, from which 98% (range 91% to 100%) of the total avail- able beats (as determined from the gold standard ECG signal) were used in the analyses. The number was reduced in the selected anal- ysis entailing both lasers (see below) to 92% (range 42% to 100%, with all but one participant exceeding 70%). The incisura (x point, corresponding to the end of systole) was identified in terms of a characteristic inflection in the differentiated velocity signals, quali- fied with respect to normative data for LVET (Polis, Smets, De Keyser, 1974; Willems Kesteloot, 1967). The displacement sig- nals were detrended prior to extracting additional measures, to sup- press slow artifacts associated with respiration and other gross movement, using a procedure whereby the trend line developed from a cubic spline fitted through the signals at the B points was subtracted (in accord with procedures sometimes applied for cor- rection of ECG baseline wander; Baldilini, Moss, Titlebaum, 1991; Meyer Keiser, 1977). LDV pulse waveform analyses. The general form of the LDV pulses is illustrated in Figure 2, where the waveforms have been selectively averaged according to segment of the respiration cycle. They have also been grand averaged over all 32 participants, after Figure 2. Grand-averaged LDV velocity (left) and displacement (right) pulses, for the four respiration cycle bins. Waveforms have been linearly time warped, to compensate for individual differences in IBI durations. Figure 1. Illustration of binning procedure for analyzing the associated cardiovascular signals according to phase of the respiration cycle. Each cycle is divided algorithmically into four segments, corresponding to inspiration active (IA; black), inspiration peak (IP; blue), expiration active (EA; red) and expiration peak (EP; green). Laser Doppler vibrometry for cardiovascular assessment 5
  • 6. linear time-warping to preserve the waveform features in the face of appreciable individual differences in IBI. For purposes of this illustration only, the longest mean IBI (for the EP segment) is arbi- trarily set to 1,000 points, with the B point (upstroke) at time 0 and preceded by 200 points. Waveforms for the other respiration seg- ments are similarly warped, but with the respective durations pre- served relative to the EP waveform. All waveforms are truncated at the time corresponding to the B point of the succeeding beat. At the left is the native velocity signal; in the right panel this has been integrated to depict a displacement signal that, for reasons described above, is broadly comparable to the pressure signal. On average, the peak mechanical displacement is small—on the order of 0.23mm and with important features in the lm range. The waveforms can be separated into phases of systole (from time 0 through the incisura at about point 300) and diastole. The B point determined algorithmically (time 0) appears somewhat anoma- lously to follow slightly the onset of the upstroke, but the desig- nated point can be determined with much greater reliability and stability than when based on attempts to detect the earliest inflec- tion point. Several additional key features are worthy of mention. These include the observation that for the EP phase the IBI is lon- gest (consistent with the directionality of RSA), the time of the incisura (at about 300 ms) is latest, and the peak amplitude as well as the amplitude during late systole is also greatest. Findings during the IP phase mirror these effects, as presented in greater detail below. Wave separation analysis. There have been many attempts over the last century to develop measures of stroke volume from the carotid pressure pulse contour (and, more recently, from peripheral sites including the radial artery and finger; see Mukkamala Xu, 2010; Thiele Durieux, 2011, for recent reviews). Early attempts were generally based on measures incorporating signal aspects throughout the duration of systole, for example, averaged ampli- tude or area measures. These methods were improved somewhat by emphasizing measures only through midsystole, recognizing that late systole in particular will include pressure waves reflected from distal sites. These reflections will increase the amplitude of the pressure pulse late in systole, but will serve to impede forward flow thus creating a period of inverse relationship between pulse amplitude and forward flow. Even more recent is the recognition that measures of carotid flow should be based principally on a period early in systole. Reports describing carotid blood flow are in agreement in showing that the peak flow rate occurs early in systole, rapidly diminishing in later systole (Brands, Hoeks, Hofstra, Reneman, 1995; Holds- worth, Norley, Frayne, Steinman, Rutt, 1999; Rabben et al., 2004). Studies in which both blood flow and pressure are simulta- neously considered illustrate that, for a brief period during the sys- tolic upstroke, the pressure and flow contours are identical (for perhaps the initial 20–30% of systole), but thereafter diverge rap- idly as the pressure contour is elevated by reflected pressure fronts (Khir, O’Brien, Gibbs, Parker, 2001; Niki et al., 2002; Parker, 2009; Rabben et al., 2004). The reflection waves later in systole are often readily identifiable as distinct components, particularly in older patients. The specific loci of the reflection sites is a matter of debate, but they likely include widely distributed impedance sour- ces in the arterioles, with possible contributions from major discon- tinuities such as the termination of the abdominal aorta (Murgo, Westerhof, Giolma, Altobelli, 1980; O’Rourke, 1982). The appearance of reflected pressure waves early in systole suggests that they possibly originate in the impedance breach in the cerebral microvasculature (Bleasdale et al., 2003), or perhaps in some early impedance discontinuity in the central elastic vessels. In any case, the close flow/pressure relationship is restricted to a brief period. In the absence of direct measures of flow (i.e., with the avail- ability of only the pressure signal, as is the case with the LDV method), a surrogate measure of flow can be extrapolated from the pressure pulse measured early in systole. This general approach was proposed by Westerhof et al. (B. E. Westerhof, Guelen, West- erhof, Karemaker, Avolio, 2006), who suggested that reflection activity be extracted with respect to a triangular equivalent flow waveform. The peak of this waveform was determined as an inflec- tion point located early in the pressure pulse upstroke, or (in the absence of a clearly demarcated inflection point) a point on the pulse waveform located at 30% of the LVET. The triangle was shown to be a reasonable approximation of aortic flow (albeit in a patient group). Subsequent refinements of the method have demon- strated the benefits of using a physiologically more plausible model of flow, based on either modeled actual flow waveforms (J. G. Kips et al., 2009; Zamani et al., 2014) or on estimated flow wave- forms based on a Windkessel-based modeling approach (Hametner, Weber, Mayer, Kropf, Wassertheurer, 2013; Pucci, Hametner, Battista, Wassertheurer, Schillaci, 2015; Weber et al., 2012). In parallel, the importance of deriving a purified measure of wave reflections must also be emphasized. The most common method for identifying the signs of increased resistance and vascular stiff- ening (as occurs, e.g., with aging) from the pressure pulse wave- form is to compute a ratio comparing the amplitude of the initial systolic upstroke peak (thought to reflect forward flow) with the “augmentation” of this peak by the addition of a later systolic peak produced by the return of the reflected pressure wave(s) (Nichols et al., 2011). This ratio is typically referred to as the augmentation index (AIx). In practice, this technique is sometimes affected by difficulties identifying key landmarks, or achieving a meaningful separation between the overlapped constituent waves. Our implementation adopted a pseudoflow waveform influ- enced by the parametric description of the carotid flow waveform provided by Holdsworth et al. (1999), informed also by estimates of peak aortic flow based on the ICG signal (Kubicek et al., 1974). The initial steps were to compute grand mean ensemble averages of both the LDV and ICG waveforms for each individual, synchronized to the LDV B point. An early segment of the LDV pulse waveform (B point through 125 ms) was cross-correlated over the early ICG waveform, lagged in 1-ms steps, to identify the optimal temporal adjustment required to synchronize the upstrokes of the ICG and LDV pulse waveforms. The peak of the ICG wave- form was then transcribed on to the LDV pulse waveform and accepted provisionally as the moment of peak flow (in the grand mean waveforms). In a subsequent step, this point was modeled and adjusted solely from features inherent in the LDV pulse wave- form (to support other applications in which the ICG signal might not be available). The approach was regression based, incorporat- ing LVET, as well as values at inflection points in the LDV pulse upstroke identified from higher-order derivatives of the displace- ment signal. The mean values for the times of this peak were in the range of 22–23% of LVET—times somewhat earlier than 30% sug- gested by B. E. Westerhof et al. (2006). A final step involved fit- ting the pseudoflow model (normalized in amplitude to the LDV displacement pulse at the time of peak pseudoflow) to eight key points derived from a published model of carotid blood flow devel- oped from Doppler ultrasound measures (Holdsworth et al., 1999), as illustrated in Figure 3 (left). Specifically, amplitude measures were taken at times corresponding to 6 E.J. Sirevaag et al.
  • 7. (a) the B point, (b) the peak of the first derivative of the upstroke, (c) half the time on the upstroke from the B point to the maxi- mum flow point, (d) the maximum flow point identified from the regression estimate, (e) a point on the descending pseudoflow waveform mirroring point (c) identified above, (f) a point on the descending pseudoflow waveform mirroring point (b) identified above, (g) a point extending an additional 10% of the remaining time to the x point (i.e., incisura), and (h) the x point marking the end of the LVET and the presumed cessation of flow. A cubic spline interpolation was then used to join these points in a smoothed pseudoflow waveform. Following development of the pseudoflow waveform, wave separation was accomplished using the procedure described by B. E. Westerhof et al. (2006), whereby forward wave 5 ðLDV carotid pulse 1 pseudo 2 flow waveÞ=2 backward wave 5 ðLDV carotid pulse 2 pseudo 2 flow waveÞ=2 The resultant decomposition is illustrated in Figure 3 (right). The decomposed forward and backward waves were in turn ana- lyzed to extract key features, utilizing lagged cross-correlation and based on the assumption that the backward wave compo- nents will be rough copies (although changed in amplitude and timing) of the forward wave. This procedure routinely yielded separate mid- and late systolic peaks in the backward wave (in addition to diastolic features that are not reported here). The amplitude of each of these peaks was computed, and used to form an augmentation ratio in terms of the ratio with the forward wave peak amplitude. The procedures described above were applied both on a beat-to- beat basis (with derived measures averaged over the respective ensemble of beats) as well as ensemble averages of the waveform data. These two sets of analyses produced generally equivalent results; the LDV measures reported below are based on the beat-to- beat analyses. The analyses of complementary psychophysiological signals (described below) were based on the identical ensembles of beats used for purposes of LDV analysis. Analysis of conventional signals. Standard methods were uti- lized to measure the ICG signal, including low-pass filtering at 20 Hz, differentiation, and inversion, and measurement of the B and x points utilizing comparable algorithms as developed for the LDV signal (described above). SV was estimated following the method of Kubicek et al. (Kubicek, Patterson, Witsoe, 1970; uti- lizing LVET derived from the LDV pulse waveform). Additional methods are described below. Statistical analysis. The presentation of data is based largely on means and associated confidence intervals (CIs), emphasizing com- parison of values at the IP and EP times, where the maximum dif- ferences were nearly always observed. CI levels of 95% are provided, one-tailed since the direction of effect could in all cases be hypothesized based on the strength of existing evidence. In the absence of accepted gold standard measures (usually invasive), we have not made extensive use of methods for formally evaluating concordance among the LDV and conventional measures (Critch- ley, Lee, Ho, 2010). Results Respiration Parameters Respiration rate was analyzed using both time-domain and frequency-domain methods, the results of which agreed closely. The time-domain measure was derived by calculating separately the median time separating consecutive inspiration peaks, and then separating consecutive expiration peaks and averaging those val- ues. This analysis yielded a mean respiration rate of 13.8 breaths per min (SD5 3.7, range 7.0 to 19.2). All but five participants had mean respiration rates 10 breaths per min. The finding of sub- stantial individual differences in rate of spontaneous breathing (as Figure 3. Illustration of wave separation procedures used to decompose LDV displacement pulse into forward (FWD) and backward (BWD) constitu- ents. Left: Original displacement pulse (black) and the pseudoflow wave formed from eight points identified from waveform features. Right: Decom- posed forward wave (blue) and backward wave (green).The end of systole (incisura) is marked by the vertical hash mark. Laser Doppler vibrometry for cardiovascular assessment 7
  • 8. well as signs of nonstationarity) is in agreement with prior observa- tions (Goodman, 1964; Lenfant, 1967). CV Measures Heart rate. Heart rate, as assessed from the LDV and the ECG signals, was analyzed variously using a number of frequency- and time-domain approaches, which agreed in confirming a close corre- spondence between rate measures obtained using the two signals. The presentation here is focused on IBI measures and their relation- ship to phase of the respiratory cycle. The character of the IBI measures derived from the LDV signal is of fundamental impor- tance, since many of the CV measures are keyed to the LDV B-point measure, and because the accuracy of the B-point measure confirms the effectiveness of the LDV method if used in the absence of conventional contact-based methods for measuring heart rate. The IBI measures derived from the ECG and LDV signals are shown in the first two rows of Table 1, which confirms that the IBI was significantly shorter during the IP than EP phases for both sig- nals, with intermediate values during the transition phases (IA and EA). The IBI effect for the LDV signal is also illustrated, along with other key LDV findings, in Figure 4. The levels of agreement between IBIs measured from the LDV and ECG signals were assessed using Bland-Altman statistics (Bland Altman, 1986). Genuine minor differences would be anticipated, since the LDV B points will include some contribution from PEP and from aortic/carotid PTT, which, as shown below, are systematically affected by phase of the respiration cycle but which are not reflected in ECG-based measures. As is clear in Figure 5, however, the PEP and PTT effects are proportionately small, and overall the agreement between LDV- and ECG-based IBI measures is very high. The correlation between mean values at each of the four respiration phases exceeded r 5 .99, and the bias never exceeded 3 ms. The level of agreement between LDV- and ECG- based measures of IBI attests to the effectiveness of the LDV anal- ysis algorithms. Systolic time intervals: LVET and PEP. The LVET measured from the LDV pulse, as the time from the B point to the x point, agreed with the parallel measure in the ICG signal in showing sig- nificantly shorter times during IP than in EP, although the absolute durations were about 17 ms shorter for ICG-based measures (Table 1, Figure 4). The origins of this difference likely lie in the algorith- mic approaches to identifying the B and x points in the respective signals. PEP was measured variously as the time from the ECG R-wave peak to the ICG B point, the parallel time to the LDV carotid pulse B point, and, more traditionally (Weissler, 1983), as the time to the onset of the second heart sound (S2) minus LVET Figure 4. Graphic illustration of effects of respiration cycle phase on several key CV measures, for the inspiration active (IA), inspiration peak (IP), expiration active (EA) and expiration peak (EP) phases. Shown are the proportionate values at each phase in comparison to the overall mean, and associated 95% CIs. (See Table 1–5.) The same verti- cal scale applies to all measures. Table 1. Mean Values and Effects of Phase of Respiration Cycle for Chronotropic Measures Chronotropic measures n Mean (raw units) IA Prop [ 6 CI] IP Prop [ 6 CI] EA Prop [ 6 CI] EP Prop [ 6 CI] EP minus IP [ 6 CI] IBI – ECG R to R 32 837.3 ms 1.000 [0.994, 1.006] 0.951 [0.943, 0.959] 1.002 [0.995, 1.009] 1.048 [1.040, 1.056] 0.097 [0.081, 0.113] IBI – LDV Bpnt to Bpnt 32 837.3 ms 1.004 [0.997, 1.010] 0.948 [0.940, 0.956] 0.999 [0.991, 1.006] 1.050 [1.042, 1.058] 0.102 [0.086, 0.118] LVET – LDV 32 272.9 ms 0.995 [0.991, 0.999] 0.975 [0.971, 0.979] 1.012 [1.008, 1.016] 1.018 [1.015, 1.021] 0.043 [0.035, 0.050] LVET – ICG 32 255.6 ms 0.990 [0.984, 0.996] 0.973 [0.968, 0.978] 1.019 [1.013, 1.025] 1.018 [1.013, 1.023] 0.045 [0.036, 0.055] R to LDV Bpnt latency 32 108.0 ms 1.003 [0.997, 1.009] 1.036 [1.028, 1.044] 0.990 [0.982, 0.997] 0.971 [0.966, 0.977] -0.065 [-0.078, -0.051] PEP – R to ICG Bpnt 32 92.8 ms 1.005 [0.997, 1.013] 1.031 [1.025, 1.038] 0.987 [0.979, 0.995] 0.977 [0.971, 0.983] -0.054 [-0.066, -0.042] PEP–- R to S2 minus LVET 30 79.3 ms 0.996 [0.988, 1.004] 1.048 [1.032, 1.065] 0.997 [0.988, 1.007] 0.959 [0.946, 0.971] -0.089 [-0.117, -0.062] Note. Shown in Column 3 are the mean values, averaged over all four phases. The values in Columns 4–7 are expressed as proportions of the mean values, for the inspiration active (IA), inspiration peak (IP), expiration active (EA), and expiration peak (EP) phases, and associated 95% CIs. The val- ues in the right column are differences between EP and IP, and associated CIs, which in no case includes zero. 8 E.J. Sirevaag et al.
  • 9. (extracted here from the LDV pulse). (The latter measure was based on 30 participants, because the phonocardiographic S2 onset could not be reliably measured in two participants. For purposes of this analysis, the S2 measurement was based on a high-pass filtered [70 Hz] and rectified version of the microphone signal.) All meas- ures concurred in showing a significant increase in PEP during the IP phase of the respiration cycle, although there was some variabil- ity among them in absolute values, probably incorporating respec- tive contributions from aortic and carotid transit times as well as measurement error. BP and inotropic CV effects. Consistent with prior findings, measures of BP, including systolic, diastolic, mean arterial, and pulse BPs, were observed to be lower during IP than EP (Figure 4, Table 2). The effects on diastolic BP were quantitatively much smaller, and differed from all other measures insofar as there was some indication of a phase shift whereby the greatest differences were seen during the active rather than peak respiration phases. The amplitude of the LDV displacement pulse, taken at the time of the peak pseudoflow wave, was substantially reduced, as was a measure of LDV stroke volume computed as the product of that amplitude and LVET (using procedures analogous to those used in the analysis of the ICG waveform). It should be qualified that the LDV stroke volume measure is uncalibrated with respect to actual flow volume. A measure of LDV cardiac output, formed as the product of the LDV stroke volume and heart rate, was not signifi- cantly affected by phase of the respiration cycle, consistent with prior observations (Toska Eriksen, 1993) that the two contribut- ing variables tend to offset each other to maintain a steady cardiac output over the respiration cycle. Similar to the pulse amplitude at the time of the pseudoflow peak, other measures of the LDV ampli- tude, including peak LDV velocity and its ratio to displacement pulse amplitude (dP/dt)/P, were smallest during IP and largest dur- ing EP phases. The ICG SV measure, in comparison, was not significantly affected by respiration phase, and in fact showed a slight, Figure 5. Bland-Altman analysis comparing mean LDV- and ECG-based IBI measures, at each of the four phases of the respiration cycle. The Bland- Altman plots were visually nearly identical if the gold standard ECG IBI rather than the mean (ECG1LDV)/2 was used for the abscissa. Laser Doppler vibrometry for cardiovascular assessment 9
  • 10. anomalous tendency to be largest during the IP phase. In view of the overwhelming evidence that stroke volume is reduced during inspiration (see the introduction), this would point to a limitation pertaining to use of ICG for the study of cardiorespiratory interac- tions (see Discussion)—and for this reason we avoid here present- ing derived measures including ICG-based measures of cardiac output or systemic resistance. Consistent with evidence reviewed earlier, the peak-to-peak amplitudes of the peripheral pulse at the ear lobe (measured photo- plethysmographically) and the dorsalis pedis artery (measured with a mechanical sensor) were substantially smaller during the IP phase. Because of signal quality issues, the dorsalis pedis pulse was based on ensemble averages, from n 5 25 participants. BCG effects. The BCG data are presented in some detail here because of the method’s potential utility as a complementary or stand-alone nonobtrusive assessment modality, involving only inci- dental contact with the chair cushion. The basic form of the BCG signal is illustrated in Figure 6. These data have been averaged over all respective candidate beats within a 3-min resting period, and grand averaged over 32 participants. In accord with the con- ventional nomenclature, the major inflections are labeled, respec- tively, as the H, I, J, and K waves. These features show a general homology with the illustrations in the literature, although rigorous identity is complicated by substantial differences in recording method, including body position and sensor characteristics. Meas- ures of the I and J waves could be obtained with highest confi- dence, and are reported here. Overall, the respiration-related effects agree with effects that have been reported previously using clinical instruments (Dinaburg Zuckerman, 1984; Starr Friedland, 1946). At IP, the latencies of the key components are reduced, and the peak-to-peak amplitudes are increased. These effects are evi- dent in Figure 6 and are depicted quantitatively in Table 3. Augmentation ratio CV effects. Computation of the augmenta- tion ratio was based on the decomposition of the LDV displace- ment pulse waveforms into the forward and backward components, using the methods described above. The amplitude of the forward component was taken at the time of the peak (which, as described Table 2. Mean Values and Effects of Phase of Respiration Cycle for Blood Pressure and Inotropic Measures Blood pressure and inotropic measures n Mean (raw units) IA Prop [ 6 CI] IP Prop [ 6 CI] EA Prop [ 6 CI] EP Prop [ 6 CI] EP minus IP [ 6 CI] BP systolic 32 111.097 mmHg 0.995 [0.991, 0.999] 0.969 [0.962, 0.976] 1.011 [1.007, 1.016] 1.025 [1.019, 1.031] 0.056 [0.043, 0.069] BP diastolic 32 63.894 mmHg 0.990 [0.987, 0.993] 0.994 [0.989, 0.998] 1.010 [1.005, 1.014] 1.007 [1.002, 1.011] 0.013 [0.004, 0.022] BP mean arterial pressure 32 95.363 mmHg 0.994 [0.990, 0.997] 0.974 [0.968, 0.981] 1.011 [1.007, 1.015] 1.021 [1.016, 1.026] 0.047 [0.035, 0.058] BP pulse pressure 32 47.203 mmHg 1.000 [0.992, 1.008] 0.935 [0.923, 0.948] 1.015 [1.006, 1.024] 1.049 [1.040, 1.059] 0.114 [0.092, 0.135] LDV pulse amp (flow point) 32 0.238 mm 1.011 [1.003, 1.019] 0.953 [0.933, 0.974] 0.995 [0.980, 1.009] 1.041 [1.027, 1.055] 0.088 [0.054, 0.121] LDV SV 32 65.126 mm.ms 1.007 [0.999, 1.015] 0.929 [0.907, 0.951] 1.008 [0.992, 1.024] 1.056 [1.041, 1.072] 0.127 [0.090, 0.163] LDV CO 32 4640.689 mm.ms.HR 1.006 [0.995, 1.016] 0.980 [0.962, 0.999] 1.007 [0.990, 1.023] 1.007 [0.994, 1.020] 0.027 [20.004, 0.057] LDV velocity peak amp 32 5.872 mm/s 1.018 [1.005, 1.032] 0.924 [0.899, 0.949] 1.011 [0.992, 1.030] 1.046 [1.030, 1.063] 0.123 [0.082, 0.164] (dP/dt)/P 32 24.941 /s 1.001 [0.992, 1.010] 0.970 [0.951, 0.989] 1.017 [1.005, 1.028] 1.012 [0.997, 1.027] 0.042 [0.008, 0.075] SV ICG 32 91.046 ml 1.006 [0.999, 1.014] 1.011 [0.997, 1.024] 0.990 [0.981, 0.999] 0.993 [0.984, 1.003] 20.017 [20.040, 0.005] Ear pulse amp (P-P) (arbitrary units) 32 91.46 0.951 [0.918, 0.985] 0.841 [0.804, 0.878] 1.092 [1.051, 1.132] 1.116 [1.087, 1.145] 0.275 [0.210, 0.340] Dorsalis pedis amp (P-P) (arbitrary units) 25 133.993 1.009 [0.996, 1.023] 0.953 [0.939, 0.967] 1.001 [0.990, 1.013] 1.036 [1.025, 1.047] 0.083 [0.060, 0.106] Note. Shown in Column 3 are the mean values, averaged over all four phases. The values in Columns 4–7 are expressed as proportions of the mean values, for the inspiration active (IA), inspiration peak (IP), expiration active (EA), and expiration peak (EP) phases, and associated 95% CIs. The val- ues in the right column are differences between EP and IP, and associated CIs, which in no case except the two indicated in italicized text (LDV CO, SV ICG) includes zero. Figure 6. Grand-averaged ballistocardiogram (BCG) signals obtained from the piezo pad in the chair cushion, for the inspiration active (IA), inspiration peak (IP), expiration active (EA) and expiration peak (EP) phases of the respiration cycle. Measures of amplitude and latency were most reliably obtained from the I and J waves. Signals are synchronized to the ECG R wave peak, at time 0. 10 E.J. Sirevaag et al.
  • 11. above, corresponded with the time of the peak of the pseudoflow waveform). The amplitude peaks of the backward waveform were taken at the times of the mid- and late systolic peaks identified using the lagged correlation methods described above. Augmenta- tion ratios computed for both peaks (especially the late systolic peak) showed a decrease during the IP phase (Figure 4, Table 4). A separate measure based on the integrated areas of the forward and backward waves (during systole) yielded similar results. Arterial timing effects. Pulse transit times were computed vari- ously from the B point of the LDV displacement pulse to the upstroke of the pulses at the ear lobe (photoplethysmographic), the radial artery (mechanical), and dorsalis pedis artery (mechanical). All showed evidence of longer latencies during the IP phase (Figure 4, Table 5), in agreement with prior evidence. PTT meas- ures were taken from the LDV B point (rather than the ECG R wave) to eliminate any contribution from PEP (Newlin, 1981). In an effort to evaluate possible local respiration-related effects on pulse wave velocity along the carotid artery, the timing of the LDV displacement pulses at the proximal and distal sites (separated by 4 cm) was compared. Different analysis approaches, based on times of the B points, x points, peaks of the velocity pulses, and lagged covariance of the systolic velocity waveforms varied somewhat with respect to absolute timing differences, but all produced gener- ally comparable findings of longer transit times during the IP phase. The values shown in Figure 4 and Table 5 are based on the lagged covariance approach. The mean transit time over the 4 cm separating the proximal and distal sites of 6.6 ms yields a carotid pulse wave velocity of slightly more than 6 m/s, generally consist- ent with values determined using other methods (Brands et al., 1995; Hermeling, Reneman, Hoeks, Reesink, 2011; Rabben et al., 2004). Latencies of the mid- and late systolic peaks in the backward wave were also analyzed, motivated by indications that the timing of the backward wave might serve as estimates of pulse wave velocity, in the form of round trip travel time (RTT; J. G. Kips et al., 2009; Qasem Avolio, 2008). These RTT timing measures were not effective in this application, showing the absence of any significant effect for the midsystolic peak and a sig- nificant effect for the late systolic peak, but in the opposite direc- tion to that shown by conventional PTT measures. Discussion The data presented here support the general effectiveness of the LDV-recorded carotid pulse as a method for measuring multiple aspects of CV function. The findings confirmed that several critical variables relating to chronotropic, inotropic, and vascular function could be measured in the LDV pulse—and that these measures agreed in most respects with expectations derived from the substan- tial literature regarding the effects of the respiration cycle on CV function and with convergent data obtained with conventional psy- chophysiological methods. The LDV measures were obtained on a noncontact basis, and the key features could be extracted algorith- mically. These findings, in turn, suggest that the LDV carotid pulse contour can be further exploited utilizing a range of analysis meth- ods that have been shown to be predictive of important clinical end points, including a variety of time- and frequency-domain model- ing approaches (Laurent et al., 2006). Several interpretive consider- ations and limitations that apply to these findings are presented briefly below. Cardiorespiratory Interactions The spontaneous breathing condition studied here is not without its issues (Ritz, 2009), but it does reliably produce signs of cardiores- piratory interaction that agree closely with those if breathing is paced at the same frequency (Bloomfield et al., 2001). In view of the inescapable variability in breaths from one cycle to the next, Table 3. Mean Values and Effects of Phase of Respiration Cycle for Ballistocardiogram Measures BCG measures n Mean (raw units) IA Prop [ 6 CI] IP Prop [ 6 CI] EA Prop [ 6 CI] EP Prop [ 6 CI] EP minus IP [ 6 CI] R to BCG I wave latency 32 113.3 ms 1.024 [1.014, 1.033] 0.923 [0.907, 0.939] 1.006 [0.997, 1.016] 1.047 [1.033, 1.061] 0.124 [0.096, 0.151] R to BCG J wave latency 32 167.9 ms 1.006 [0.996, 1.015] 0.985 [0.971, 1.000] 0.999 [0.985, 1.013] 1.010 [0.998, 1.023] 0.025 [0.002, 0.048] BCG amplitude (I-J P-P) (arbitrary units) 32 301.15 1.048 [1.025, 1.071] 1.144 [1.090, 1.198] 0.932 [0.898, 0.967] 0.876 [0.820, 0.931] 20.269 [20.373, 20.164] Note. Shown in Column 3 are the mean values, averaged over all four phases. The values in Columns 4–7 are expressed as proportions of the mean values, for the inspiration active (IA), inspiration peak (IP), expiration active (EA), and expiration peak (EP) phases, and associated 95% CIs. The val- ues in the right column are differences between EP and IP, and associated CIs, which in no case includes zero. BCG 5 ballistocardiogram. Table 4. Mean Values and Effects of Phase of Respiration Cycle for Augmentation Ratio Measures Augmentation ratio n Mean (raw units) IA Prop [ 6 CI] IP Prop [ 6 CI] EA Prop [ 6 CI] EP Prop [ 6 CI] EP minus IP [ 6 CI] Augmentation ratio mid 32 0.732 0.994 [0.971, 1.016] 0.974 [0.943, 1.005] 0.981 [0.961, 1.001] 1.051 [1.026, 1.075] 0.077 [0.024, 0.130] Augmentation ratio late 32 0.724 0.934 [0.907, 0.962] 0.903 [0.857, 0.949] 1.028 [0.998, 1.058] 1.135 [1.097, 1.172] 0.231 [0.151, 0.311] Augmentation ratio total 32 1.456 0.967 [0.947, 0.987] 0.941 [0.910, 0.972] 1.004 [0.987, 1.022] 1.088 [1.063, 1.113] 0.147 [0.093, 0.200] Note. Shown in Column 3 are the mean values, averaged over all four phases. The values in Columns 4–7 are expressed as proportions of the mean values, for the inspiration active (IA), inspiration peak (IP), expiration active (EA), and expiration peak (EP) phases, and associated 95% CIs. The val- ues in the right column are differences between EP and IP, and associated CIs, which in no case includes zero. Laser Doppler vibrometry for cardiovascular assessment 11
  • 12. spontaneous breathing poses difficulties for frequency-based meth- ods and limitations on the resolution with which phase relation- ships can be determined (although these problems may not be intractable, e.g., Orini, Laguna, Mainardi, Bailon, 2012). These issues notwithstanding, the procedure used here, involving binning according to phase of the respiration cycle, was adopted because of its simplicity, and indeed it proved to reveal highly significant effects in multiple measures. The typicality of the data obtained here was confirmed by the compatibility with previously reported findings (as reviewed in the introduction). These included, during inspiration, the cardinal signs of fall in BP and decrease in IBI. The IBI measures were nearly equivalent, whether based on the conventional R wave of the ECG, or on algorithmically detected times of LDV carotid pulse upstroke, attesting to the effectiveness of the algorithms for proc- essing the LDV signal. LVET was also reduced by similar amounts in both the LDV carotid pulse and ICG signals (Frey Doers, 1983). The LVET reduction during inspiration (in conjunction with lengthening of the right ventricular ejection time; Leighton et al., 1971; van Leeuwen Kuemmell, 1987) produces the splitting of the S2 heart sound that is freely auscultated in the clinical exam (Castle Jones, 1961; Ehlers, Engle, Farnsworth, Levin, 1969; Felner, 1990; Yildrim Ansari, 2007)—and which was readily identifiable here in the phonocardiograms of 23 of the 32 partici- pants. The pattern of inspiratory decrease in LVET (Karlocai et al., 1998; Nandi et al., 1973; van Leeuwen Kuemmell, 1987), and increase in left ventricular PEP (Johansson et al., 2006; Nandi et al., 1973; van Leeuwen Kuemmell, 1987), is commonly attrib- uted to reduced left ventricular filling as would apply during the inspiration phase of the respiration cycle. The attempt to derive measures related to changes in SV from the pressure pulse follows an extensive tradition of similar efforts (Mukkamala Xu, 2010; Thiele Durieux, 2011), but as imple- mented here might offer the advantage of being based on the puri- fied forward wave component of the pressure pulse, and thus relatively unaffected by reflected pressure fronts in the backward wave. The derived measure of LDV SV responded in the antici- pated manner; the finding of a 13% change over the respiration cycle is in line with findings by other investigators using other methods, although the specific magnitude of the effect on SV is affected by the rate and depth of respiration (Guz et al., 1987). In this regard, it is notable that the ICG method did not provide evidence of the anticipated reduction in SV during inspiration; indeed, there was a small (and nonsignificant) anomalous trend in the opposite direction. We obtained a similar pattern in the face of several attempts at filtering or detrending the gross pneumoimpe- dance disturbances from the ICG signal (Hahn, Sipinkova, Baisch, Hellige, 1995). The impact of respiration on ICG measures is widely recognized and is often considered a source of artifact that is typically dealt with by restricting sampling to beats falling in periods of end-expiratory eupnea (Miller Horvath, 1978), by ensemble averaging across the respiratory cycle (Ekman et al., 1990; Muzi et al., 1985), or by attempts at filtering and adaptive cancellation (Pandey Pandey, 2005; Raza, Patterson, Wang, 1992; Yamamoto et al., 1988). The small increase in ICG measures of SV during inspiration seen here is evident in the findings of other investigators, although not always attaining statistical signifi- cance (Davies et al., 2000; Doerr, Miles, Frey, 1981; Du Ques- ney, Stoute, Hughson, 1987; L. Wang, Patterson, Raza, 1991). The causes are unclear. Although interpretations of the source of the ICG signal tend to emphasize flow in the aortic column (Bern- stein, 2010; Kubicek, 1989; Sherwood et al., 1990), the possible, and perhaps large, contribution of cardiodynamics in the pulmo- nary circuit has also been cited (Denniston et al., 1976; Miles Gotshall, 1989; Noordegraaf et al., 1998; Patterson, 1985; Saito, Goto, Terasaki, Hayashida, Morioka, 1983; Wang Patterson, 1995). In any case, these findings point to significant complications in any attempt to use the ICG method to evaluate cardiorespiratory interactions. The BCG signal appeared to be similarly affected by phase of respiration, insofar as the major component (I-J wave) was signifi- cantly larger and earlier during inspiration. This finding is in accord with those of other investigators (Dock, 1962; Starr Friedland, 1946; Starr et al., 1939; Williams Gropper, 1951), using tradi- tional BCG recording techniques, who have interpreted them as showing that the BCG signal can be ascribed at least in part to right ventricular ejection that is increased in volume and with shortened PEP during inspiration (Brecher Hubay, 1955; Gabe et al., 1969; Kim et al., 1987; Lauson et al., 1946; Leighton et al., 1971). The measures related to vascular dynamics present interpretive challenges. PTT, measured variously using conventional measures as well as in terms of the transit time between two LDV pulses Table 5. Mean Values and Effects of Phase of Respiration Cycle for Arterial Timing Measures Vascular timing n Mean (raw units) IA Prop [ 6 CI] IP Prop [ 6 CI] EA Prop [ 6 CI] EP Prop [ 6 CI] EP minus IP [ 6 CI] PTT – LDV Bpnt to ear lobe 32 82.3 ms 1.013 [1.006, 1.020] 1.036 [1.024, 1.049] 0.972 [0.964, 0.980] 0.979 [0.970, 0.988] 20.057 [20.078, 20.037] PTT – LDV Bpnt to radial artery 32 102.9 ms 1.006 [1.002, 1.011] 1.030 [1.020, 1.039] 0.985 [0.981, 0.989] 0.979 [0.972, 0.986] 20.051 [20.0667, 20.035] PTT – LDV Bpnt to dorsalis pedis artery 25 175.3 ms 1.005 [1.001, 1.008] 1.014 [1.007, 1.021] 0.991 [0.986, 0.996] 0.990 [0.986, 0.995] 20.024 [20.035, 20.013] PTT – Proximal to distal LDV 32 6.6 ms 1.019 [0.994, 1.044] 1.068 [1.010, 1.126] 0.976 [0.957, 0.995] 0.937 [0.891, 0.982] 20.132 [20.234, 20.029] Midsystolic reflection latency 32 122.6 ms 0.997 [0.992, 1.002] 0.999 [0.989, 1.008] 0.996 [0.993, 0.999] 1.008 [0.999, 1.017] 0.009 [20.009, 0.028] Late systolic reflection latency 32 228.8 ms 0.999 [0.996, 1.003] 0.980 [0.974, 0.987] 1.010 [1.006, 1.014] 1.010 [1.006, 1.014] 0.029 [0.020, 0.039] Note. Shown in Column 3 are the mean values, averaged over all four phases. The values in Columns 4–7 are expressed as proportions of the mean values, for the inspiration active (IA), inspiration peak (IP), expiration active (EA), and expiration peak (EP) phases, and associated 95% CIs. The val- ues in the right column are differences between EP and IP, and associated CIs, which in no cases except midsystolic reflection latency includes zero (indicated in italicized text). 12 E.J. Sirevaag et al.
  • 13. along the carotid, agreed in showing longer times (i.e., slower PWV) during inspiration. As computed for the foot, radial, and ear measures (all with respect to the B point of the LDV carotid pulse), a possible contribution from PEP was eliminated. While it is possi- ble that these effects derive from changes in vascular tone over the respiration cycle, an interpretation in terms of macrocirculatory factors is also plausible. The direct relationship between PWV and BP is well established (Weltman et al., 1964), and on this basis the slower PWV during inspiration could be explained in terms of the concomitant lowered BP. Arterial stiffness is increased at higher BPs (Cunha, Benetos, Laurent, Safar, Asmar, 1995; Laurent et al., 1994) even over the course of the blood pressure cycle (J. K.-J. Li, Cui, Drzewiecki, 1990). The possibility that HR may contribute to the changes in PWV (as well as features of the pulse contour) can also be considered. Although the observed effects and associated interpretation have been variable and depend on measurement site (e.g., Wilkinson et al., 2000), prior evidence from pacing (Haesler, Lyon, Pruvot, Kappenberger, Hayoz, 2004; Liang et al., 1999; S. C. Millasseau, Stewart, Patel, Redwood, Chowienczyk, 2005) and cross-sectional studies (Sa Cunha et al., 1997) have pointed to a direct relationship between HR and PWV—findings that are oppo- site to the relationship observed here (i.e., decreased PWV but increased HR during inspiration). The passive nature of these changes in PWV is further sup- ported by our findings of parallel changes along the 4-cm distance separating the two LDV targeting sites on the carotid. It was observed that the local PWV of 6 m/s was reduced by about 12% during the inspiration phase, in comparison to expiration. Given that the carotid in humans is almost solely an elastic vessel, the stiffness of which is regulated by the respective engagement of elastin and collagen fibers rather than muscular function (Bonyhay et al., 1997), and questionable involvement of endothelial factors (Horvath, Pinter, Kollai, 2012), it is unclear how the rapid within-cycle respiration changes in carotid PWV could be mediated by some active process. From a technical perspective, it is worth noting that carotid PWV is the subject of intense interest because of promising clinical applications (Konofagou, Lee, Luo, Provost, Vappou, 2011), and thus the ability to measure it from the LDV signal may be of significant value. A dedicated multiple beam LDV system for measuring carotid PWV has been described (Campo Dirckx, 2011). Additional measures of timing were derived here from mid- and late systolic contour features of the dis- tal LDV carotid pulse, with the intent of investigating the possibil- ity that the latencies could serve as measures of vascular RTT of the pressure wave to and from an equivalent reflection site (Qasem Avolio, 2008). Although there have been some reports of a close relationship between PWV and RTT (Qasem Avolio, 2008; B. E. Westerhof et al., 2006), these findings and their interpretation remain controversial (Baksi et al., 2009; Gurovich, Beck, Braith, 2009; J. G. Kips et al., 2009; B. E. Westerhof, van den Wijngaard, Murgo, Westerhof, 2008). The analysis here did not produce evi- dence for the expected relationship between RTT and PWV. Whereas the midsystolic peak was unaffected, the late systolic peak paradoxically was decreased in latency during the inspiration phase, that is, in the opposite direction from other vascular timing measures. An important qualification is that the analysis here can- not be generalized to other more conventional implementations of RTT, which are based on detection of a midsystolic inflection point taken as the earliest arrival of the major reflection wave. There are related questions of interpretation that apply to the measure of augmentation ratio, which was operationalized here in terms of the ratio of amplitudes of the separated backward and for- ward waves. This ratio is conceptually similar to the AIx, which is usually measured in terms of the amplitudes late and early in sys- tole. AIx is determined by the relative amplitude of the reflected wave, which in turn is thought to be determined by the level of impedance encountered peripherally (and perhaps the speed with which the reflected wave returns; Nichols Singh, 2002). Consist- ent with this interpretation, the magnitude of the reflection wave that contributes to AIx is commonly found to increase with aging as well as hypertension and other clinical end points (Nichols Singh, 2002), and also to show acute increases upon noradrenergic stimulation including the cold pressor test (Casey, Braith, Pierce, 2008; Liu et al., 2011). Overall, the augmentation ratios observed here were small, consistent with expectations when recording from young healthy individuals (Nichols et al., 2011) in the seated rather than supine body position (van den Bogaard et al., 2011; Vrachatis et al., 2014). Nevertheless, the augmentation ratio showed signifi- cant effects of respiration cycle. It was found to be larger during the expiration phase than during inspiration—an effect that, if taken at face value, would suggest that systemic resistance increased dur- ing expiration. As noted above, the ICG-based cardiac output measurements were not adequate to support computation of sys- temic resistance; a substitute analysis utilizing cardiac output based on the LDV SV found that the attendant “systemic resistance” was increased marginally (about 2%) during EP compared to inspira- tion. This difference was not significant at the time of IP (EP minus IP, 6 CI) 5 0.018 (-0.010, 0.047), although was significant if the comparison was based on IA (EP minus IA, 6 CI) 5 0.026 (0.009, 0.0440). The relevant evidence regarding this appearance of dynamic vascular adjustment over the respiration cycle is equivocal. Studies of sympathetic nerve traffic have found evidence for respiration- related rhythmicity in sympathetic drive to the cutaneous, muscu- lar, and splanchnic vascular beds (Cogliati, Magatelli, Montano, Narkiewicz, Somers, 2000; Eckberg, Nerhed, Wallin, 1985; Malpas, 1998; Pilowsky, 1995), which in turn may derive from central and reflex mechanisms including pulmonary stretch reflexes (Looga, 1997) and baroreceptor reflexes (G. G. Wallin Charkou- dian, 2007). These findings have led some investigators to accept the existence of respiration-related changes in resistance; Limberg et al. (Limberg, Morgan, Schrage, Dempsey, 2013), for example, cite evidence leading them to conclude that “powerful within- breath respiratory modulation of sympathetic vasoconstrictor activ- ity has been well documented in humans and experimental ani- mals” (p. H1615). The effects at the vasomotor level have also, however, been shown to depend strongly on multiple factors including species differences, specific vascular bed, and ventilation parameters, and to show complex phase relationships with the res- piration cycle. Of particular importance is respiration rate, with the vasomotor effects being most prominent at slow rates (Seals, Suwarno, Dempsey, 1990; Stauss, Anderson, Haynes, Kregel, 1998). This is in accord with the broad indications of general slug- gishness of the sympathetic vasomotor signal transduction effects, which include multisecond delays observed in the sympathetic response to abrupt stimulation (Toska, Eriksen, Walloe, 1994; B. G. Wallin Eckberg, 1982), and the effective low-pass filter on rhythmic activities with a corner frequency estimated to be in the range of 0.1 to 0.2 Hz (Bernardi et al., 1997; Julien, Malpas, Stauss, 2001; Rosenbaum Race, 1968; Saul et al., 1991). On bal- ance, our findings would admit the possibility of some vascular resistance effects, probably more evident in the slow-breathing par- ticipants in this study (with the slowest at 0.12 Hz), but perhaps Laser Doppler vibrometry for cardiovascular assessment 13
  • 14. even in some form (albeit attenuated) in the faster-breathing individuals. Measurement Issues Recording the carotid pulse. As cited above, the usefulness of the carotid pulse lies in its close similarity to the central (aortic) BP pulse, which is valuable because of the information it conveys regarding myocardial performance, afterload, vascular compliance, and diastolic function (Nichols et al., 2011). The external carotid pulse has been the subject of an extremely large clinical and scien- tific literature (Nichols et al., 2011; Tavel, 1972), and has been suc- cessfully utilized in large-scale studies of clinical end points (e.g., Rietzschel et al., 2007). The carotid measurement site has the addi- tional advantages of its intimate relevance for cerebral circulation and carotid baroreceptor function (Steinback, O’Leary, Wang, Shoemaker, 2004). There are, however, limiting factors in practice. Recording the carotid pulse using tonometric, ultrasound, photo- electric, and mechanical sensors is described as raising issues regarding operator training, repeatability, patient comfort, hold- down pressure, and the small but nonnegligible risk of disrupting plaque (J. Kips et al., 2010; S. Millasseau Agnoletti, 2015; O’Rourke, Pauca, Jiang, 2001). For these reasons, the more com- mon method is to estimate the central BP on the basis of a transfer function applied to the pulse recorded from some peripheral artery (usually radial, where the underlying bony support facilitates flat- tening of the artery for tonometric recording). The various elements surrounding use of the transfer function have been the subject of debate. Of greater concern when using the carotid site, especially to psychophysiologists, are the possible autonomic consequences associated with external massage and pressure applied to the carotid baroreceptors—strong enough that carotid massage has tra- ditionally been an option for bedside testing of autonomic failure (Schweitzer Techholz, 1985). In this critical regard, the LDV method, in which there is the complete elimination of any contact, would appear to offer an advantage. It is important to frame this with the recognition that the diameter changes that form the bases of the LDV carotid pulse signal, while closely related to BP, are not equivalent. The differences arise from nonlinearities in the carotid pressure-distension curve, following the deviations from pure elasticity produced by the respective engagement of elastin and collagen fibers as diameter changes (London Pannier, 2010; Meinders Hoeks, 2004; S. Millasseau Agnoletti, 2015). It is also important to note that the carotid LDV pulse at the skin is uncalibrated with respect to absolute diastolic and systolic pressure values, since it will be affected by such factors as distensibility of the carotid artery, intervening subcutaneous tissue, precision of laser targeting, and small in-plane changes in position during the recording period. The signal could in principle be calibrated with respect to pressure at a brachial or radial site (Agnoletti et al., 2012; Vermeersch et al., 2008), although calibrating with respect to noninvasive BP measures is itself a matter of debate (Adji O’Rourke, 2012; Hope, Meredith, Cameron, 2004). Even when interpreted as an uncalibrated pressure pulse waveform, however, a considerable amount of information relating to such variables as cardiac output and vascular dynamics can be adduced—especially with respect to changes in the measures on an intraindividual basis. LDV metrological considerations. The extent of agreement between signals obtained using the LDV method and other nonin- vasive methods remains to be determined, but some general consid- erations can be offered. A fairly large number of commercial noninvasive methods for measuring central BP have been described (S. Millasseau Agnoletti, 2015; Narayan et al., 2014). The method dependency of the derived measures is widely acknowl- edged—leading one editorial commentator to cite the “likelihood that as many different estimated . . . central BP parameters can be obtained as there are devices designed to obtain them” (Cameron, 2013, p. 27). A major consideration pertains to the frequency response, which is generally higher in the intraarterial BP signal than is accurately represented in the tonometrically derived transfer functions for estimating central BP (Laurent et al., 2006). As another example, BP variability measured with the Finapres method has been found to underestimate variability in the high fre- quency respiration band, while overestimating it at lower frequen- cies in comparison to the intraarterial pressure signal (Kornet, Hoeks, Janssen, Willigers, Reneman, 2002). A noteworthy feature of the LDV method in general is the broadband frequency response, capable of transducing mechanical energy (in the case of the Polytec IVS-300) through 22 kHz. This compares with conventional tonometric sensors, which are limited in use to an effective upper frequency of about 5 Hz, despite nomi- nally greater capability in the sensor itself (Matthys Verdonck, 2002; Sato, Nishinaga, Kawamoto, Ozowa, Takatsuji, 1993). Perhaps consistent with the broad frequency response is the high degree of texture observed here in the LDV carotid pulse wave- form. At least three systolic peaks (the forward wave and two in the backward wave) were reliably measured, but typically there were additional inflections and peaks that were readily apparent. It is possible that some of these peaks might reflect mechanical activ- ity referred from more distal sites—perhaps including gross BCG influences, although it should be noted that none of the major BCG components (see Figure 5), if adjusted for the time between R wave and LDV carotid B point, could be readily mapped onto the LDV carotid pulse waveform. Other investigators, using different measurement techniques, have also described the presence of mul- tiple reflection and rereflection waves in the central BP pulse (Baruch, Kalantari, Gerdt, Adkins, 2014; Berger, Li, Laskey, Noordbergraaf, 1993; Wang, Xu, Feng, Meng, Wang, 2013). There are also higher-frequency signals present in the LDV sig- nal, albeit usually of much lower amplitude. Time-frequency repre- sentations of the LDV carotid pulse invariably show time-locked energy at frequencies exceeding 100 Hz. The energy includes local vessel sounds (Hasegawa, Rodbard, Kinoshita, 1991), used here as an initial basis for detection of candidate pulses. These sounds can appear in the form of bruits in the presence of atherosclerosis of the carotid (Pickett, Jackson, Hemann, Atwood, 2008). Also possibly apparent are even higher sounds including poststenotic wall vibrations exceeding 2 kHz—the study of which appears to be among the earliest examples of using the LDV for a biological sens- ing application (Stehbens, Liepsch, Poll, Erhardt, 1995). Because of the high acoustic impedance mismatch between skin and air, little if any vibration activity from ambient sounds can be impressed on the skin to interfere with these measures (Katz, 2000). Relevance for psychophysiological studies. These findings add to the growing body of evidence regarding the range of physiologi- cal signals, all of interest to psychophysiology, that can be recorded using the LDV method (and kindred methods based on self-mixing interferometry and laser speckle tracking). In keeping with the gen- eral observation that physiology (particularly at the system level) typically includes an appreciable mechanical component, these applications have demonstrated effectiveness in a number of 14 E.J. Sirevaag et al.
  • 15. systems including respiration (Marchionni, Scalise, Ercoli, Tom- asini, 2013; Scalise, Ercoli, Marchionni, 2010; Scalise, Ercoli, Marchionni, Tomasini, 2011; Scalise, Marchionni, Ercoli, 2010), speech (Avargel Cohen, 2011), mechanical myogram (Rohrbaugh, Sirevaag, Richter, 2013; Scalise, Casaccia, March- ionni, Ercoli, Tomasini, 2013), and biomechanics (P. Castellini Tomasini, 1998; Nataletti, Paone, Scalise, 2005; Revel, Scal- ise, Scalise, 2003; Scalise, Rossetti, Paone, 2007; Valentino et al., 2004), in addition to the CV system. CV variables include the phonocardiogram (De Melis, Morbiducci, Scalise, 2007), PTT (De Melis et al., 2008), heart rate (Marchionni et al., 2013; Morbiducci, Scalise, De Melis, Grigioni, 2007; Scalise Morbid- ucci, 2008), movements of the precordium (Hong Fox, 1997; Scalise, Morbiducci, De Melis, 2006; Schuurman, Rixen, Swenne, Hinnen, 2013), and the BP pulse (Campo, Segers, Dirckx, 2011; Capelli, Bollati, Giuliani, 2011; Desjardins, Antontelli, Soares, 2007; Hong Fox, 1994, 1997; Y. Li, Segers, Dirckx, Baets, 2013; Pinotti, Paone, Santos, Tomasini, 1998). An attractive feature of all of these applications for psychophy- siological purposes lies in the noncontact nature of the method, which supports testing in the absence of attached sensors, or rigid requirements for body position (although active motion including speech and orofacial movement poses problems for currently avail- able methods). The study reported here entailed preparation of the skin as well as the physical attachment of additional sensors, but these encumbrances are not required by, or inherent in, the LDV method if used on a stand-alone basis. LDV systems are available in a range of configurations, providing beam steering, scanning, multiaxial (3D), and autofocus capabilities. Scanning systems pro- vide extensive capabilities for data analysis in time, frequency, and spatial domains. The data presented here are based primarily on a Polytec single-point industrial vibrometer (IVS 300). Detailed specifications for this and other systems are available at the Polytec website (http://www.polytec.com/us/). The IVS-300 system pro- vides an analog velocity output signal, which in the case of this study was processed using custom algorithms as described above. 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