1. An Exploration of Heart Sound Denoising Method Based on Wavelet and Singular Spectrum Analysis Name: ZENG Tao Supervisor: Prof. DONG Mingchui University of Macau, Faculty of Science and Technology Department of Electrical and Electronics Engineering

2. 2 Outline  Introduction  Motivation & Contributions  Heart Sound (HS) Denoising Methods  Traditional wavelet shrinkage (TWS)  Dynamic threshold wavelet shrinkage (DTWS)  Singular Spectrum Analysis (SSA)  Test Result  Conclusion  Future Work

3. Introduction 3 Introduction

4. “The number of people who die from CVDs, mainly from heart disease and stroke, will increase to reach 23.3 million by 2030.” “An estimated 17.3 million people died from CVDs in 2008, representing 30% of all global deaths.” Introduction 4 Cardiovascular diseases (CVDs)

5. Introduction 5 Technology for CVDs diagnosis Echocardiogram (ECHO) Electrocardiography (ECG) Phonocardiogram (PCG) Sphygmogram (SPG)

6. Introduction 6 Why PCG (recording of HS) ? InformationConveniencePre-diagnosis SPGMediumHighMedium PCGHigh Strong ECGHighMedium ECHOMediumLowMedium (a)Normal HS: S1 and S2 without murmurs (b) Abnormal HS: S1 and S2 with murmurs Murmurs: Indicate pathological information Periodic and low amplitude

7. Murmurs are submerged 7 Introduction Raw Signal Qualified Signal Improve HS signal quality. Decrease difficulty and increase accuracy in following steps. Schematic diagram of E-home healthcare system (EHS):

8. 8 Introduction Noise Classification:  White noise (Ideal and not that practical)  Power line noise (Can be eliminated by circuits)  Respiratory sounds (Overlap in frequency domain)  Environmental noise (Irregular but common)  Pulse noise (Finger touch during measurement)  Press noise (Stethoscopes contact with skin) Stationary noise Irregular noise

9. 9 Motivation 1. The various noise components make the diagnostic evaluation of PCG records difficult or in some cases even impossible. 2. TWS method based on wavelet transform (WT) is only applicable to eliminate stationary noise (e.g. White noise) in normal HS, which is not practical. 3. Irregular noises (e.g. Environmental noises) may appear and original HS is probably abnormal with murmurs. An advanced HS denoising scheme is necessary for the implementation of EHS.

10. Contributions 10 1. Develop the dynamic threshold wavelet shrinkage (DTWS) based on traditional wavelet shrinkage (TWS) to further retain the foremost HS and murmurs information while eliminating stationary noises utmostly. 2. Adapt the singular spectrum analysis (SSA) to extract principal component (PC) of HS to enhance denoising performance of HS signals with irregular noises. 3. Construct testing and evaluation environment, comparing the SNR and RMSE improvement between TWS and the proposed denoising methods.

11. TWS Method 11 TWS Method

12. 12 Objective: Eliminate stationary noise (e.g. white noise) from original HS signal. Principle: Utilize different characteristics between non-stationary signal and stationary signal after WT in wavelet domain. Problems: 1.For abnormal HS with murmurs, it’s easy to distort pathologic information by mistaking murmurs as noises. 2. Bad performance on irregular noises elimination.

13. TWS Method 13 Wavelet domain: w i T heu : Heuristic threshold Based on heursure rule Determined by WC of layers. Each layer has certain value. a: Approximation coefficients (AC) d: Detail coefficients (DC) Soft shrinkage rule Schematic diagram of TWS Wavelet coefficients(WC) Threshold setting

14. DTWS Method 14 DTWS Method

15. 15 Objective: Solve first problem of TWS, retain the foremost HS and murmurs information while eliminating stationary noises utmostly.

16. DTWS Method 16 Same to TWSDTWS Developed part

17. DTWS Method 17 Characteristic Layer (CL) Selection Objective: Find the decomposition layers contain most HS and murmurs information (CL). Principle: Untilize the periodicity of HS and murmurs, identifying CL by autocorrelation: where r j and s j (n) are the autocorrelation result and reconstructed signal of jth decomposition layer respectively, l represents signal length, and ƞ is the step length. (a)Autocorrelation results of periodic signal (b)Autocorrelation results of aperiodic signal NCL CL

18. DTWS Method 18 Dynamic Threshold shrinkage Dynamic Objective: Set dynamic threshold to shrink WC at CLs. Principle: Utilize the average Shannon energy to further separate HS and murmurs information from stationary noises. WC correspond to noise: Larger threshold WC correspond to HS and murmurs: Smaller threshold

19. DTWS Method 19 Performance comparison Original HS with murmurs Denoising result of DTWS Denoising result of TWS Test by adding white noise to original HS, which satisfies SNR = 0 dB retained distorted

20. DTWS Method 20 Problems: 1.For abnormal HS with murmurs, it is easy to distort pathologic information by mistaking murmurs as noises. 2.Bad performance on irregular noises elimination.

21. SSA 21 SSA

22. SSA 22 Objective: Solve second problem of TWS, eliminate irregular noise from HS signal and retain murmurs information simultaneously.

23. SSA 23 Time series Trajectory Matrix Eigenvalues Effective Threshold Embedded Dimensions Descending curvature of eigenvalues Effective threshold

24. SSA 24 Original HS with murmurs Denoising result of SSA Denoising result of TWS Test by adding environmental noise to original HS, which satisfies SNR = 0 dB S4 gallop retained

25. SSA 25 Problems: 2.Bad performance on irregular noises elimination.

26. Test Result 26 Test Result

27. 27 Normal HS with Stationary Noises Pure HS signal Original HS signal After preprocessing After DTWS After SSA

28. Test Result 28 Normal HS with Irregular Noises Pure HS signal Original HS signal After preprocessing After DTWS After SSA

29. Test Result 29 Abnormal HS with Stationary Noises Pure HS signal Original HS signal After preprocessing After DTWS After SSA

30. Test Result 30 Abnormal HS with Irregular Noises Pure HS signal Original HS signal After preprocessing After DTWS After SSA

31. Test Result 31 SNR and RMSE Evaluation Normal HS + Stationary noise: SNR 0.52%; RMSE 1.35% Abnormal HS + Stationary noise: SNR 12.92%; RMSE 6.96% Normal HS + Irregular noise: SNR 219.35%; RMSE 45.50% Abnormal HS + Irregular noise: SNR 263.00%; RMSE 52.97%

32. Conclusion 32  DTWS methods and SSA eliminate stationary noise and irregular noise from original HS and retains pathological murmur information simultaneously.  The proposed scheme improves denoising performance in SNR and RMSE comparing to TWS method.  Denoising performance of HS with irregular noises has the most significant improvement, which corresponds to more practical situations.

33. Future Work 33  Construct professional HS database with more site- sampled HS recordings (PCG).  Test more HS samples to adjust parameters in proposed HS denoising scheme.  Send qualified HS signals after denoising to HS analysis system for subsequent analysis.

34. Publications 34  Tao ZENG, JiaLi MA, BinBin FU, and MingChui DONG, “An Exploration of Dynamic Threshold Wavelet Shrinkage Method for Heart Sound Denoising”, 3rd International Conference on Electronics Engineering and Informatics (ICEEI 2014), Bali, Indonesia, Sep. 2014. (Accepted on 13th of May, 2014)  Tao ZENG, JiaLi MA, BinBin FU, and MingChui DONG, “Irregular Noise Elimination of Heart Sound Based on Singular Spectrum Analysis”, to be submitted.

35. University of Macau, Faculty of Science and Technology Department of Electrical and Electronics Engineering Q & A