Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Decomposition of the Cardiac and Respiratory Components from Impedance Pneumography Signals

203 views

Published on

BIOSIGNALS 2017 Presentation - Publication by Marcel Młyńczak and Gerard Cybulski

Published in: Health & Medicine
  • Be the first to comment

  • Be the first to like this

Decomposition of the Cardiac and Respiratory Components from Impedance Pneumography Signals

  1. 1. Decomposition of cardiac and respiratory components from 
 impedance pneumography signals 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 Physiology measurements Respiratory and cardiac systems activity monitoring. Long-term measurements Accuracy and applicability Point-in-time measurements do not allow for proper evaluation. Classic spirometry could not be applied in Holter-type applications. Impedance pneumography provides the possibility to perform comfortable testing, with precision close to direct gold standard method. 2
  3. 3. The Problem • Impedance pneumography is usually carried out using current tetrapolar method. • Voltage electrodes are positioned on the midaxillary line at about 5th-rib level. • It is similar setting to the one used in ambulatory ECG recordings. • Cardiac component is observed in the IP signals. • Preprocessing methods could impact on the recorded signal by: ‣ trying to remove the non-zero mean value of the cardiac component between the beginning of P wave and the ending of T wave ‣ degrading the correspondence between respiratory IP component and reference. 3
  4. 4. The Problem Sample of the raw IP signal with both respiratory and cardiac components 10 15 20 25 30 35 40 Time [s] 7000 7500 8000 8500 9000 9500 RawImpedance[mOhm] Impedance (with ECG component) 4
  5. 5. The Problem The comparison of basic filtration methods applied on raw IP signal 1000 1500 2000 2500 3000 3500 4000 Time [probes] 53 54 55 56 57 58 59Impedance[Ohm] Raw signal Very soft median filtered signal Moving avarage smoothing (1.5s) Savitzky-Golay smoothing 5
  6. 6. Related works • Savitzky-Golay smoothing (Savitzky and Golay, 1964) • Adaptive removing based on simultaneous recording of ECG 
 (Seppa et al., 2011; Schuessler et al., 2008) • Smoothing splines (Reinsch, 1967; Schoenberg, 1964; Poupard et al., 2008) • Filtration of noncorrelated noise in impedance cardiography (Barros et al., 1995) • EMD or EEMD (Wang et al., 2016) • Wavelet denoising • Adaptive filtering and Scaled Fourier Linear Combiner (SFLC) (Yasuda et al., 2005) Still all mentioned algorithms are mainly intended to remove cardiac component, not to preserve both! 6
  7. 7. Objectives 1. 4.3. 5.2. • To assess the quality of various preprocessing methods, 
 which could be applied on raw IP signal in order to separate respiratory and cardiac components. • To indicate the most robust algorithm from both respiratory 
 (volume-related parameters), and cardiac perspective (HR or HRV). Main What calibration procedure could provide the best data for further measurements? Is cardiac component comparable to the single-lead ECG signal in terms of heart rate calculation? What are inspiratory and expiratory tidal volumes (TVin & TVex) for testing data? What is the analysis duration and complexity? What are determination coefficients (R2) of the calibration model? 7
  8. 8. Methodology Subjects - generally healthy students, 10 males 8 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
  9. 9. Methodology Devices & Electrode configuration • Flow Measurement System with a Spirometer Unit and a Fleisch-type 
 Heatable Flow Transducer 5530, with a Conical Mouthpiece (Medikro Oy, Finland) • Pneumonitor 2 (IP, ECG, Accelerometry) 9 IP electrodes
  10. 10. Methodology Analysis scheme IP PNT Filtering Detrending Integration Breathing phases establishing Applying calibration coefficients Preprocessed IP Integrated PNT Calibration IP Filtering Detrending Preprocessed IP Decomposition PNT Integration Integrated PNT ECG HRV curve estimating & analysis Cardiac IP Respiratory IP Inspiration & Expiration TV calculating & analysis Calibration Main measurements 10
  11. 11. 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 11 1 2 3
  12. 12. Methodology Test procedure 12 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.
  13. 13. Methodology Test procedure 13 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. 4
  14. 14. Breathing phases Established from reference, integrated pneumotachometry signal 0 50 100 150 200 -2000 -1000 0 1000 2000 3000Volume[ml] 0 50 100 150 200 Time [s] -2 -1 0 1 2 Breathingphase 14
  15. 15. Methodology Considered decomposition methods ( I ) 15 • Moving avarage smoothing ➡ 0.5 second window (considered mild) ➡ 1 second window (Koivumaki et al., 2012) ➡ 1.5 second window (consdered strong) • Savitzky-Golay filtering ➡ 2nd-order filter with a 25 probes window (Savitzky and Golay, 1964). ➡ 7th-order filter with a 25 probes window. • Least mean square adaptive filtration ➡ subtraction of raw IP signal and the noise component, 
 then smoothed with 200 ms window. ➡ subtraction of raw IP signal and the noise component, 
 then smoothed with 400 ms window.
  16. 16. Methodology Considered decomposition methods ( II ) 16 • Impulse response filtration ➡ 25-fold decimation (performed twice using 5-fold coefficient), 
 then applying 10th order least-square FIR filter with 1 Hz pass and 
 2.5 Hz stop frequencies, at the end the spline interpolation to 
 return to original sampling frequency ➡ the same process as above, but with use of 10th order stable 
 Chebyshev IIR 1 Hz pass frequency filter • Wavelet denoising ➡ soft heuristic SURE thresholding and scaled noise option, 
 on detail coefficients obtained from the decomposition at level 5 
 by ’sym8’ wavelet ➡ minimax thresholding at level 5 by ’db5’ wavelet • Smoothing Splines (Reinsch, 1967)
  17. 17. Methodology • Calibration model assumed linear relationship between respiratory component of IP and reference pneumotachometry, without considering intercept value. • Tidal volumes were assessed separately for inspirations and expirations 
 (based on breathing phases established earlier) (Poupard et al., 2008). • The accuracies were calculated as mean percentage error (relative to reference). • ECG signals were analysed only for supine body position; 
 R points were automatically marked using simple thresholding technique. • The possibility to extract the R points from cardiac IP component was linked with 
 the estimation of the equivalent of signal-to-noise ratio. • The processing time of the algorithms were measured with the computer processor Intel i5 (1200MHz), without any accelerations. • All analyses were performed using MATLAB 2016b software. Other remarks 17
  18. 18. Results Sample relationship between IP respiratory component and reference -40 -30 -20 -10 0 10 20 30 40 50 Impedance after mean removal [Ohm] -2000 -1500 -1000 -500 0 500 1000 1500 2000 2500 Volumeaftermeanremoval[L] Measurement points Linear fitting 18
  19. 19. Results Mean determination coefficients of the linear calibration model [%] Algorithm Procedure 1
 30 second free breathing Procedure 2
 2 minute free breathing Procedure 3
 Fixed breathing Procedure 4 Reference measurement Moving 
 average 94,2 91,1 92,2 92,1 95,3 91,2 92,3 92,2 94,4 89,6 92,0 92,0 Savitzky-Golay filtering 82,8 82,7 90,8 90,2 79,5 79,9 90,1 89,5 Adaptive 
 filtration 90,0 88,9 91,8 91,5 93,3 90,6 92,1 91,9 Impulse response filtering 93,1 90,6 92,0 91,8 3,7 11,4 46,8 50,2 Wavelet 
 denoising 91,2 89,1 91,6 91,2 88,7 87,2 91,6 91,1 Smoothing splines 92,7 87,2 91,6 91,6 19
  20. 20. Results Mean processing time of the decomposition calculation Algorithm Procedure 1
 30 second free breathing Procedure 2
 2 minute free breathing Procedure 3
 Fixed breathing Procedure 4 Reference measurement Moving 
 average smoothing [ms] 3,4 7,1 11,4 18,3 3,4 9,9 11,6 15,9 3,4 11,7 13,1 19,3 Savitzky-Golay filtering [ms] 3,4 12,3 13,5 18,3 3,4 11,6 12,5 17,3 Adaptive 
 filtration [s] 0,17 1,06 1,77 3,52 0,16 1,06 1,70 3,73 Impulse response filtering [ms][s] 24,4 26,0 36,3 42,3 0,15 0,14 0,17 0,19 Wavelet 
 denoising [ms] 29,9 37,6 43,5 56,1 24,8 29,8 27,9 38,8 Splines [s] 1,63 25,53 45,16 106,09 20
  21. 21. Results Bland-Altman plot for tidal volumes for both inspirations and expirations Sample for all participants, for each body positions, for 1st calibration procedure and for 7th algorithm. 21
  22. 22. Results The comparison of tidal volume estimating accuracy for the best algorithms Algorithm Error Procedure 1 Procedure 2 Procedure 3 Procedure 4 Moving average (0.5s window) Absolute [ml] 214,7 240,5 251,0 165,3 Relative [%] 13,8 16,0 17,1 11,8 Moving average (1s window) Absolute [ml] 206,0 245,7 284,2 205,7 Relative [%] 13,5 16,7 19,3 14,4 Least mean square adaptive filtration, smoothed 
 400ms window Absolute [ml] 234,3 251,2 240,1 153,0 Relative [%] 14,9 16,5 16,5 11,2 22
  23. 23. Results Sample comparison between IP cardiac component and reference ECG 0 10 20 30 40 50 60 1000 2000 3000 Arbitraryunits ECG Reference (with R points found) 0 10 20 30 40 50 60 -4 -2 0 2 4 Impedance[Ohm] Cardiac IP Component (with R points found) 0 10 20 30 40 50 60 Time [s] 40 60 80 100 120 Heartrate[BPM] HRV curves Derived from Cardiac IP Component Derived from ECG Reference 23
  24. 24. Results The comparison of HR and HRV curve derived from IP cardiac component and reference The minimal overall error of cardiac calculations from IP was obtained for third algorithm. 2 3 7 12 Procedure -5 0 5 10 RelativeError[%] 24
  25. 25. Results Sample comparison between IP cardiac component and reference ECG There were no statistically significant correspondence between the 
 accuracy of cardiac calculations from IP signals, and the SN ratio. 2 3 7 12 Procedure 0 0.2 0.4 0.6 0.8 1 Cross-correlation 25
  26. 26. Discussion Respiratory accuracy or cardiac accuracy or processing time… Which optimization criteria to choose? Separate approaches? Which calibration procedure to carry out? Different decomposition method for both respiratory and cardiac part… Short and comfortable, or long and more complex one… 26
  27. 27. Conclusions Mean 86.5% accuracy of tidal volume calculating and 
 only 2.7% error of heart rate estimation were obtained using 
 moving average smoothing filters, for simple short recording of 
 free breathing calibration procedure, in three body positions. More sophisticated adaptive filtering also provided good accuracy, however the processing time was 100-times higher, comparing to simple methods. Cardiac component is not equally visible in every participant, however obtained compatibility between ECG reference seems promising, particularly concerning ambulatory measurements. 27
  28. 28. Discussion • ”Dynamic” measurements, which 
 imitate natural functioning of subjects. • Further improvement and assessment 
 of the decomposition methods and their accuracy, e.g., using time series algo- rithms utilized in econometrics field. • Evaluation of the possibility to remove the classical ECG registration from ambulatory cardiorespiratory measurements. • There were only 10 participants, 
 only males. • The measurements were carried out 
 only in static conditions, without the need to consider motion artifacts. • The reference ECG signal was 
 single-lead one. • In ambulatory situations, the registrations are longer and would be more diversified, which may affect the overall accuracy. Limitations of the study Further plans 28
  29. 29. Porto, February 23, 2017 Marcel Młyńczak, MSc mlynczak@mchtr.pw.edu.pl Decomposition of cardiac and respiratory components from 
 impedance pneumography signals

×