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

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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

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