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# Flow Parameters Derived from Impedance Pneumography after Nonlinear Calibration based on Neural Networks

BIOSIGNALS 2017 Presentation - Publication by Marcel Młyńczak and Gerard Cybulski

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### Flow Parameters Derived from Impedance Pneumography after Nonlinear Calibration based on Neural Networks

1. 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. 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. 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. 4. The Problem The ﬂow IP signals are usually underestimated 4
5. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 22. Summary Recovery of dynamics 22
23. 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. 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. 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. 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 26
27. 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. 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