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Time Frequency Analysis and Wavelet Transforms Oral Presentation

Time Frequency Analysis and Wavelet Transforms Oral Presentation. Applications of Discrete Wavelet Transform in ECG Signal Processing. Presenter: Chia-Chun Hsu 徐嘉駿 E-mail: r04942075@ntu.edu.tw. Graduate Institute of Communication Engineering National Taiwan University Taipei, Taiwan

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Time Frequency Analysis and Wavelet Transforms Oral Presentation

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  1. Time Frequency Analysis and Wavelet Transforms Oral Presentation Applications of Discrete Wavelet Transform in ECG Signal Processing Presenter:Chia-Chun Hsu 徐嘉駿 E-mail: r04942075@ntu.edu.tw Graduate Institute of Communication Engineering National Taiwan University Taipei, Taiwan November 26,2015

  2. Outline • Introduction to ECG signal • Motivation • Discrete Wavelet Transform • Conventional Discrete Wavelet Transform • Stationary Discrete Wavelet Transform • ECG Signal Processing • Baseline Wandering Problem • Power line noise and noise interference • Feature detection • Reference

  3. Outline • Introduction to ECG signal • Motivation • Discrete Wavelet Transform • Conventional Discrete Wavelet Transform • Stationary Discrete Wavelet Transform • ECG Signal Processing • Baseline Wandering Problem • Power line noise and noise interference • Feature detection • Reference

  4. Introduction to ECG signal • Electrocardiography(ECG) is the process of recording the electrical activity of the heart on graph sheets or some monitors over a period of time by placing the electrodes on some specific locations of body of a person. Fig1. ECG monitor From: http://www2.ntin.edu.tw/constructing/%E6%80%A5%E9%87%8D%E7%97%87%E8%AD%B7%E7%90%86%E8%A8%AD%E5%82%99.htm

  5. Introduction to ECG signal • Relationship between the polarization of the heart and the ECG signal. Fig2. Animation of a norml ECG wave From: https://en.wikipedia.org/wiki/Electrocardiography

  6. Introduction to ECG signal • Normal ECG signal Fig3. Normal ECG signal

  7. Introduction to ECG signal Table1 FeatureofECGsignalanditsdescription

  8. Outline • Introduction to ECG signal • Motivation • Discrete Wavelet Transform • Conventional Discrete Wavelet Transform • Stationary Discrete Wavelet Transform • ECG Signal Processing • Baseline Wandering Problem • Power line noise and noise interference • Feature detection • Reference

  9. Motivation • The ECG signal is a very weak signal, amounting to only 0.5 and 2mV at the skin surface. • Hence, ECG signal is usually contaminated by the external disturbance.(e.g. Power line noise, respiration, etc.)

  10. Motivation • Problem of ECG signal processing • Power line noise • High frequency noise interference • Baseline wander problem • QRS complex extraction

  11. Motivation • Power line noise(PLI): In general , Power line noise is caused by the electromagnetic field of 50/60Hz power line. • American Heart Association recommends that ECG recorder should have a 3dB frequency range extending from 0.67 to 150Hz.

  12. Motivation Fig4. ECG signal with 50Hz PLI noise

  13. Motivation • High frequency noise interference Fig5. ECG signal contaminated by high frequency noise

  14. Motivation • Baseline wander(BW):Commonly caused by electrode-skin impedance changes due to perspiration, patient movement, and respiration. From:MIT-BIH Arrhythmia Database Fig5. Baseline wander of ECG signal

  15. Motivation • Flow chart of ECG signal processing ECG signal Baseline correction Noise removal QRS complex extraction Determine the symptoms. Classification (e.g. M.L) P,Q,S,T extraction Note : M.L = Machine Learning

  16. Outline • Introduction to ECG signal • Motivation • Discrete Wavelet Transform • Conventional Discrete Wavelet Transform • Stationary Discrete Wavelet Transform • ECG Signal Processing • Baseline Wandering Problem • Power line noise and noise interference • Feature detection • Reference

  17. Discrete Wavelet Transform • Conventional discrete wavelet transform(DWT) L-points down sampling lowpass filter xL[n] ~N/2-points g[n]  2 x1,L[n] N-points L-points x[n] highpass filter down sampling xH[n] ~N/2-points x1,H[n]  2 h[n] In general, N>>L. x1,L[n]:Approximation(Low frequency part) x1,H[n]:Details (high frequency part)

  18. Discrete Wavelet Transform • The multi-level decompositions of the DWT loss the characteristic of the original signal at the high level. (a) (b) Fig6. (a)Non-stationary signal (b) DWT of (a) [1]

  19. Stationary Discrete Wavelet Transform • The stationary discrete wavelet transform (STW) is similar to the DWT but without the decimation. • Filters in each level are up-sampled version of the previous.

  20. Stationary Discrete Wavelet Transform (b) (a) Fig7. (a)A 3 level SWT filter bank (b) SWT filters →The up-sampling scheme is achieved by inserting zeros between every adjacent pair of elements of the filter.

  21. Outline • Introduction to ECG signal • Motivation • Discrete Wavelet Transform • Conventional Discrete Wavelet Transform • Stationary Discrete Wavelet Transform • ECG Signal Processing • Baseline Wandering Problem • Power line noise and noise interference • Feature detection • Reference

  22. ECG Signal Processing-Baseline problem(1/5) • Baseline problem: Baseline can be viewed as the low frequency part in the ECG signal. Fig8. Noisy sine wave

  23. ECG Signal Processing-Baseline problem(2/5) • Conventional methods using moving average filter to estimate the baseline. • DWT use the filter banks for construction of the multi-resolution analysis. [2] • Separate the ECG signal into its Approximation and Details. →Sensitive to the abrupt peak.(e.g. R peak)

  24. ECG Signal Processing-Baseline problem(3/5) • From MIT-BIH Database [8] • (b) (c) (d) • (b) (c) (d) Fig9. Each level of SWT result (a)Original signal (b)Level1 (c)Level 5 (d)Level9

  25. ECG Signal Processing-Baseline problem(4/5) • MATLAB Code

  26. ECG Signal Processing-Baseline problem(5/5) Fig10. Examples of baseline drift correction. (Wavelet method)

  27. ECG Signal Processing-Noise Removal(1/7) • For the power line noise, the notch filter is used to filter out the 50/60Hz noise. • To deal with the high frequency noise problem, the naïve method is using the Low pass filter.

  28. ECG Signal Processing-Noise Removal(2/7) • Wavelet approach(proposed by Donoho)[3] • Hard thresholding • soft thresholding →Noise : High frequency(detail) with small amplitude →R peak : High frequency(detail) with larger amplitude than the noise.

  29. ECG Signal Processing-Noise Removal(3/7) (a) (b) (c) Fig10. Denoising of ECG signal by using wavelet transform thresholding techneque.[4] (a)Raw data (b)Noisy ECG signal (c)Denoising by wavelet transform

  30. ECG Signal Processing-Feature detection (4/7) • In ECG feature detection, R-wave peak is the most important job. • When the R peak postion is found, the location of P,Q,S,T can be found by the relative position from R peak to each others.

  31. ECG Signal Processing-Feature detection (5/7) • Time domain detection [5] • Derivative method + peak height thresholding • Frequency domain detection • Hilbert Transform method [6] • Discrete Wavelet Transform Method [7] →Very sensitive to the noise.(Your pre-processing must have good performance)

  32. ECG Signal Processing-Feature detection (6/7) • Discrete Wavelet Transform Method: In order to detect the R-wave peak, using DWT to separate the ECG signal to details(High Frequency) and approximation(Low frequency). • Find the R peak characteristic in the detail parts of DWT.

  33. ECG Signal Processing-Feature detection (7/7) Fig11. Multiresolution decomposition of ECG signal Using D6 containing short burst of noise. Level5 has high similarity to the R peak!!→

  34. Reference [1] Nason, Guy P., and Bernard W. Silverman. "The stationary wavelet transform and some statistical applications." LECTURE NOTES IN STATISTICS-NEW YORK-SPRINGER VERLAG- (1995): 281-281. [2] Chowdhury, Shubhajit Roy, and Dipankar Chakrabarti. "Daubechies wavelet decomposition based baseline wander correction of trans-abdominal maternal ECG." Electrical and Computer Engineering (ICECE), 2010 International Conference on. IEEE, 2010. [3] Lin, H-Y., et al. "Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals." IRBM 35.6 (2014): 351-361. [4] Georgieva-Tsaneva, Galya, and Krassimir Tcheshmedjiev. "Denoising of electrocardiogram data with methods of wavelet transform." International Conference on Computer Systems and Technologies. 2013.

  35. Reference [5] J.P.Pan, “A Real-Time QRS Dection Algorithm”, IEEE Transaction Biomedical Engineering, pp. 230-236, 1985. [6] Benitez, D., et al. "The use of the Hilbert transform in ECG signal analysis."Computers in biology and medicine 31.5 (2001): 399-406. [7] S. Mahmoodabadi , A. Ahmadian , M. Abolhasani , M. Eslami and J. Bidgoli  "ECG feature extraction based on multiresolution wavelet transform",  Proc. IEEE Eng. Med. Biol. Soc.,  pp.3902 -3905 2005  [8] Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001). Available from: https://www.physionet.org/physiobank/database/mitdb/ [9] 國立台灣大學電信工程研究所，數位影像與訊號處理實驗室，課程專區，時頻分析與小波轉換，Tutorial專區:Time-Frequency Analysis for ECG signals。

  36. Thank you for your attention!!

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