BIOSIGNALS 2017 Presentation - Publication by Marcel Młyńczak and Gerard Cybulski

1. Flow parameters derived from
impedance pneumography after nonlinear
calibration based on neural networks
Marcel Młyńczak, MSc, Gerard Cybulski, PhD
Warsaw University of Technology, Faculty of Mechatronics,
Institute of Metrology and Biomedical Engineering
Porto, February 23, 2017

2. Introduction
Spirometry / Pneumotachometry
• The most reliable methods
• Direct measurements
• The need for using a face mask or mouthpiece with nose clip
• Uncomfortable during exercises, sleep and for children
Figures adapted from chat.stackexchange.com and legio24.pl
Mesh grid of
known pneumatic
resistance
2

3. Introduction
Impedance pneumography
Basic idea
Changes of transthoracic bioimpedance are
connected with changes of the amount of air in the lungs.
Method of
measurements
The nature of the
IP signals
Carried out using tetrapolar method.
Volume-related.
Calibration
Simple linear model provides the best accuracy of volume
parameters for specific electrode configuration.
Flow
measurements
Obtained from differentiated
impedance pneumography signals.
3

4. The Problem
The flow IP signals are usually underestimated
4

5. The Problem
Possible solution
Nonlinear signal calibration?
To recover the dynamics of the flow signal artificially by using
nonlinear processing of the signal.
5

6. Objectives
• Improvement of the accuracy of flow parameters:
➡ peak flow values, separately for inspirations and expirations
➡ mean flow values, as above
calculated by impedance pneumography, using neural network approach.
• Assessment of which neural network structure is the best for the flow
parameters analysis.
• Finding the best calibration procedure in terms of flow-related analysis.
6

7. Methodology
Subjects - generally healthy students, 10 males
Min Avg Max
Weight [kg] 65.0 77.4 100.0
Height [cm] 171.0 179.3 187.0
BMI 20.75 24.14 33.41
Age 19 23 27
7

8. Methodology
Pneumonitor 2
• ECG signal to estimate heart rate
and tachogram
• Impedance signal relating to
main breathing activity
• Portable
• Recording on SD card
• Rechargeable battery
• Motion signal from 3-axis
accelerometer to indicate
subject’s activity and body position
8

9. Methodology
Electrode configuration Reference device
Flow Measurement System with
a Spirometer Unit and a Fleisch-type
Heatable Flow Transducer 5530,
with a Conical Mouthpiece
(Medikro Oy, Finland)
9
IP electrodes

10. Methodology
• The simplest and the quickest one
➡ Free breathing registered for 30 seconds.
• To evaluate the impact of longer measurement
➡ Free breathing registered for 2 minutes.
• To check, whether adding various rates and depths of breathing may
improve the calibration quality meaningly
➡ Fixed breathing, shallow and deep alternately, 4 times each,
for three frequencies: 6, 10 and 15 breaths per minute (BPM).
Each calibration procedure was repeated for three body positions:
• supine
• sitting
• standing
Calibration procedures
10
1
2
3

11. Methodology
Test procedure
11
Consisting of 6 breaths with two subjectively different depths:
➡ normal
➡ deep
for three breathing rates:
➡ 6 BPM
➡ 10 BPM
➡ 15 BPM
and for three body positions:
➡ supine
➡ sitting
➡ standing.

12. Methodology
Signal processing
12
• Finding breathing phases
• Differentiation
➡ by second-order method.
• Smoothing
➡ Moving average filter with a 500 ms window, striking a balance
between (necessary) removal of the cardiac component and
(undesirable) deterioration of the signal dynamics.

13. Methodology
Neural Network
Perceptron architecture with classic Levenberg-Marquardt backpropagation
learning method and default division of data into sets:
• training (70%)
• validation (15%)
• test (15%)
13

14. Methodology
Postprocessing
14
• Post-hoc smoothing (to preserve the discontinuities that may arise)
➡ Moving average filter with a 125 ms window
• MATLAB 2016b toolboxes

15. Methodology
Considered calibration approaches ( I )
15
• Simple linear modeling based on flow-related signals (as a reference)
• Neural network approach, trained individually:
➡ single hidden layer with 10 or 20 neurons
➡ two hidden layers of 5 or 10 neurons
• Simple linear modeling and neural network correction, trained individually:
➡ single hidden layer with 10 or 20 neurons
➡ two hidden layers of 5 or 10 neurons
• Simple linear modeling and neural network correction, trained globally:
➡ single hidden layer with 10 or 20 neurons
➡ two hidden layers of 5 or 10 neurons

16. Results
Comparison for the most accurace calibration approaches in each group ( I )
Calibration Procedure 1 - Free breathing registered for 30 seconds
16
Linear
model
(reference)
NN, trained
individually
(10, 10)
Linear model
+ NN, trained
individually
(10)
Linear model
+ NN, trained
globally
(20)
Peak flow
Mean absolute error [ml/s] 240.7 220.9 216.7 174.3
Mean relative error [%] 34.6 24.8
Mean flow
Mean absolute error [ml/s] 187.6 169.8 173.5 145.2
Mean relative error [%] 43.7 30.3

17. Results
Comparison for the most accurace calibration approaches in each group ( II )
Calibration Procedure 2 - Free breathing registered for 2 minutes
17
Linear
model
(reference)
NN, trained
individually
(10, 10)
Linear model
+ NN, trained
individually
(10, 10)
Linear model
+ NN, trained
globally
(10)
Peak flow
Mean absolute error [ml/s] 142.6 175.6 200.1 128.1
Mean relative error [%] 26.6 18.9
Mean flow
Mean absolute error [ml/s] 130.8 147.5 155.2 118.5
Mean relative error [%] 31.6 26.0

18. Results
Comparison for the most accurace calibration approaches in each group ( III )
Calibration Procedure 3 - Fixed breathing
18
Linear
model
(reference)
NN, trained
individually
(10, 10)
Linear model
+ NN, trained
individually
(10)
Linear model
+ NN, trained
globally
(20)
Peak flow
Mean absolute error [ml/s] 144.3 118.4 118.5 133.0
Mean relative error [%] 28.6 20.8
Mean flow
Mean absolute error [ml/s] 102.8 90.5 87.7 84.3
Mean relative error [%] 25.8 21.4

19. Results
Comparison for the most accurace calibration approaches in each group ( IV )
19
Linear
model
(reference)
NN, trained
individually
(10, 10)
Linear model
+ NN, trained
individually
(10)
Linear model
+ NN, trained
globally
(20)
Peak flow
Mean absolute error [ml/s] 144.3 109.1 109.7 123.7
Mean relative error [%] 28.6 18.4
Mean flow
Mean absolute error [ml/s] 102.8 91.4 88.6 85.0
Mean relative error [%] 25.8 21.6
Calibration Procedure 3 - Fixed breathing (with post-hoc smoothing)

20. Results
Calibration Procedure 3
Fixed breathing
(with post-hoc smoothing)
Neural network, trained indivudually
(10, 10)
Bland-Altman plots for the 2 best approaches
20
Calibration Procedure 3
Fixed breathing
Linear modeling and neural network,
trained indivudually
(10)

21. Results
Time of analysis
• Individual neural network learning over 14 times faster than global
• Average 36s for individual vs 4min 12s for global learning
• Intel i5 class processor
21

22. Summary
Recovery of dynamics
22

23. Summary
80% accuracy of peak and mean flow parameters estimation:
➡ for NN with two hidden layers of 10 neurons,
➡ based on the data from the longest, the most complex
calibration procedure with fixed breathing
versus 72.5% for simple linear modeling.
Neural networks trained individually seem to be more reliable and
provide better results than ones trained with a global set.
23

24. Discussion
Quantitative respiratory parameters
Figures adapted from Clevend Clinic Medical & Wikicommons materials
24
Breathing frequency [ l/min ]
Flow-volume parametersTidal volume [ l ]
Minute ventilation [ l/min ]

25. Discussion
Quantitative respiratory parameters
25
What about flows during natural breathing?
Breathing frequency [ l/min ]
Flow-volume parametersTidal volume [ l ]
Minute ventilation [ l/min ]

26. Discussion
Ambulatory respiratory monitoring
Sleep Physiology Sport medicine
• Hypo-, normo-, and
hyperventilation monitoring
in the obese and those with
neuromuscular diseases
• Cardiorespiratory coupling
analysis
• In-house diagnostics
• Training control
• Determining the level of
exercice
Figures adapted from ”Pulmonary Function Testing” Rolf M. Schlegelmilch, Rüdiger Kramme, Springer, 2011
• Monitoring of breathing
disorders
• Analysis of the effects of
pharmacological treatment
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27. Discussion
• Validation of different sequence of
preprocessing and other nonlinear
algorithms.
• Preparation of models established
separately for inspirations and
expirations, and for different depths
of breathing.
• Measurements only in static
conditions, without considering
motion artifacts.
• Neural network approach was not
compared with other nonlinear
methods.
Limitations of this study Further plans
27

28. Flow parameters derived from
impedance pneumography after nonlinear
calibration based on neural networks
Porto, February 23, 2017
Marcel Młyńczak, MSc
mlynczak@mchtr.pw.edu.pl