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Motion artifact detection in respiratory signals based on Teager energy operator and accelerometer signals

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Presentation from EMBEC'17 & NBC'17 conference

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Motion artifact detection in respiratory signals based on Teager energy operator and accelerometer signals

  1. 1. Motion artifact detection in respiratory signals based on Teager energy operator and accelerometer signals Marcel Młyńczak and Gerard Cybulski Warsaw University of Technology, Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering Tampere, June 14, 2017
  2. 2. Introduction 2 Ambulatory measurements Sleep recordings Indirect but quantitative method Sense of using impedance pneumography
  3. 3. Introduction 3 Contact method using electrodes Need for calibration, dependent on body position Motion artifacts appearances Remarks / Drawbacks
  4. 4. Introduction 4 Motion artifacts Frequency range of an artifact signal 
 partly overlaps that of breathing Amplitude is usually uncorrelated with 
 motion intensity and characteristics Shape may be repeatable, 
 but not in the same way in each subject
  5. 5. Introduction • Spectral or wavelet features • Signal decomposition • Kalman filtering • Adaptive method based on ε-Tube The problem had not been studied in depth for 
 estimation of quantitative respiratory parameters! 5 Related works
  6. 6. Objectives •Presentation of novel method • relatively computationally simple • adapted to IP signal characteristics to detect motion artifacts using • Teager energy operator • additional 3-axis accelerometer signals •Assessment of its accuracy relative to manual marking 6
  7. 7. Materials and Methods 7 Study group - 24 students (12 females and 12 males) Female Male Mean SD Mean SD Mass [kg] 58.6 5.6 76.2 9.5 Weight [cm] 168.2 6.2 178.8 5.6 BMI 20.7 1.6 23.9 3.3 Age 22.3 5.3 22.9 3.2
  8. 8. 8 Study protocol 4 free breaths 
 for consecutive 
 body positions: ➡ supine ➡ side ➡ prone ➡ side ➡ supine ➡ sitting ➡ standing ➡ sitting ➡ supine Materials and Methods
  9. 9. Materials and Methods Pneumonitor 2 9 • 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
  10. 10. Materials and Methods Electrode configuration - as suggested by Seppa et al. IP electrodes ECG electrodes Motion sensor 10
  11. 11. Algorithm 11 Raw IP signal Motion 3-axis signals Teager-Kaiser Energy Operator Aggregation TKE Moving average smoothing 40 milliseconds window Moving average smoothing 48 milliseconds window … Moving average smoothing 1 second window -||- -||- -||- Summation of absolute derivatives of each axis MRD Spectrogram estimation Summation of frequency content for consecutive time portions TKEspecNormalization Interpolation Normalization Avg Std Weight [kg] 58.6 5.6 Height [cm] 168.2 6.2 BMI 20.7 1.6 Age 22.3 5.3 the arms at the same level [12]. Movements the belt. A sampling frequency of fs = 250H B. Motion artifact detection A schematic of the proposed motion a method is presented in Figure 1. The pro based on the continuous Teager–Kaiser en was originally introduced by Kaiser et al. as ator to measure instantaneous energy change sisting of a single time-varying frequency [13 eral applications to detect sudden changes, sets, were later proposed [15, 16]. The continuous Teager-Kaiser energy ope x in time t is calculated as follows: TKE (x) = ✓ dx dt ◆2 +x d2x dt2 It was found that the calculated energy both amplitude and frequency. Therefore, th phasizes both instantaneous properties [14]. * * * * * * * 1st step Manually annotated motion artifacts Supine Side Prone Side Supine Sitting Standing Sitting
  12. 12. Algorithm 12 Kaiser perator Aggregation TKE on of absolute s of each axis MRD Spectrogram estimation Summation of frequency content for consecutive time portions TKEspec Final reas Normalization Interpolation Envelope d TKEe THR Normalization Hann window 
 with 32 samples 2nd step
  13. 13. Algorithm 13 3rd step * * * * * * * Manually annotated motion artifacts
  14. 14. Algorithm 14 Smoothed envelope estimated 
 using the Hilbert transform 4th step
  15. 15. Algorithm 15 Raw IP signal Motion 3-axis signals Teager-Kaiser Energy Operator Aggregation TKE Moving average smoothing 40 milliseconds window Moving average smoothing 48 milliseconds window … Moving average smoothing 1 second window -||- -||- -||- Summation of absolute derivatives of each axis MRD Spectrogram estimation Summation of frequency content for consecutive time portions TKEspec Final reasoning Normalization Interpolation Envelope detection TKEenv THR Normalization
  16. 16. Sample signal and analysis 16
  17. 17. Results 17 Classification results for the best setting Accuracy [%] 81.3 Cohen’s kappa coefficient 0.63 Sensitivity [%] 80.9 Specificity [%] 81.3
  18. 18. Discussion 18 State-of-the-art motion artifact detection methods are poorly suited for 
 respiratory data, because they usually assume some regularities in the signals. Accelerometer signals are certainly not necessary, but could be used to 
 adapt the threshold, intended to determine whether some part of a signal 
 is more likely to have motion artifacts. In our opinion, presented method could be improved by: • changing the thresholding approach into continuous analysis using time series methods, machine learning heuristics, and/or Kalman filtering fusion method; • verifying the accuracy improvement by making the whole analysis 
 fully independent on the respiratory-related impedance amplitude; • adding wavelet-based parameters to the analysis, with wavelet shapes closely resembling those most often observed as motion artifacts.
  19. 19. Summary We obtained 82% accuracy versus manual marking 
 for the optimal combination of threshold level estimation and assumptions made to utilize motion signals. Proposed method could be applied in real time during ambulatory signal acquisitions. 19
  20. 20. Motion artifact detection in respiratory signals based on Teager energy operator and accelerometer signals Tampere, June 14, 2017 Marcel Młyńczak mlynczak@mchtr.pw.edu.pl

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