this presentation overviews about heart rate variability and its uses as a biofeedback mechanism in varying the heart rate and thus releaving stress and other applications of it

1. HEART RATE VARIABILITY
• DATTARAJ . SANSGIRI –
10-548
• RAGHAV. NARASIMHAN
– 11-501
• JUI . AGAM – 11-502

2.  Variations in time interval between heartbeats.
 Measured by variations in beat-tot-beat interval.

3.  This is done either by using ECG recording or PPG
graphs or Blood Pressure curves.
 ECG recording along with Blood Pressure curves is
used normally to be accurate.

5.  The Sino-Atrial node
generates such
impulses 100-120 times
per min !!!

6. Sympathetic Nervous
• Fight and Flight.
• Stimulates heart’s
functioning.
• High sympathetic
stimulation = High Heart
Rate and Stroke Volume.
• Heart’s response to
Sympathetic Stimulation =
5 secs.
Para-Sympathetic Nervous
• Calmness and Rest.
• Inhibits heart’s functioning.
• Acts only after Sympathetic
Stimulation has taken place.
• Heart instantaneously
responds to para-
Sympathetic Stimulation

8.  Bio-feedback mechanism.
 When changes are monitored and displayed on a computer
screen, clearly indicate how radically our thoughts and
emotions affect the heart and nervous system.

9. Factors affecting normal functioning of the Heart :
 Respiration
 Thermoregulation
 Blood Pressure
 Cardiac Output.

10.  TIME – DOMAIN ANALYSIS
 FREQUENCY – DOMAIN ANALYSIS

12.  Calculates a number of variables that describe either the
heart rate at any time or determine the intervals between
successive normal complexes.
 In a continuous electrocardiographic (ECG) recording each
QRS complex is detected and the normal-to-normal (N-N)
intervals or the instantaneous heart rate are determined.
TIME DOMAIN ANALYSIS

13. Some simple calculated time domain variables include :
 Mean NN interval
 Mean heart rate
 Difference between the shortest and the longest N-N interval

14.  Standard deviation of N-N intervals (SDNN) (estimate of
overall HRV)
 HRV triangular index (estimate of overall HRV)
 Standard deviation of averages of N-N intervals
(SDANN)
(estimate of long-term components of HRV)
 Square root of the mean of squared deviation(RMSSD)
(estimate of short-term components HRV).

18. Acting like a mirror reflecting emotional states this real time feedback
can help us prevent, manage and reverse the harmful effects of stress and
negative emotions.
It is believed that Heart Rate Variability (HRV) will become as common
as pulse, blood pressure or temperature in patient charts in the near future.
It is found that lowered HRV is associated with aging, decreased
autonomic activity, hormonal tonus, specific types of autonomic neuropathies
(e.g. diabetic neuropathy) and increased risk of sudden cardiac death after acute
heart attack.

19.  Heart rate variability (HRV), a measure of autonomic function, can predict
survival outcomes.
 Heart rate, measured as beat-to-beat intervals, is not constant and varies
in time. This property is known as heart rate variability (HRV) and it has
been investigated in several diseases.
 These include :
 Diabetes
 Cardiovascular Diseases
 Myocardial Infraction
 Detecting Cancer

20. As a complication of diabetes
mellitus, Autonomic Neuropathy is
developed.
Early and widespread neuronal
degeneration of small nerve fibres of
both sympathetic and parasympathetic
tracts.

21. Reduced heart rate recovery (HRR) in coronary artery disease (CAD)
is predictive of increased cardiovascular mortality and is related to reduced
parasympathetic tonus.
HRR and HRV are significantly reduced in CAD.
The reduction in HRR is parallel to the changes in HRV parameters.
HRR, which can be measured easily in the recovery phase of exercise
testing, can be used to detect the depression of parasympathetic tonus and
to evaluate the basal autonomic balance in this patient group.

24. It is well-known that HRV is the strongest indicator of the
autonomic function. Lowering certain HRV parameters may tell us
that parasympathetic tonus is in trouble.
It cannot be used to make specific diagnoses. It serves as
a strong marker of the autonomic health and estimates the
possibility of developing cancer when the autonomic function is
chronically low.
Helps catching a negative tendency in the autonomic
function way before the first symptoms of cancer may occur.

27. RAGHAV.NARASIMHAN
BE – BIOM