Heart sound denoising by dynamic noise estimation_Journal of Biomedical Engineering

Authors：

 XU Chundong ¹ , ZHOU Jing ¹ , YING Dongwen ^1,2 , XIN Pengli ¹

1. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, P.R.China;
2. School of Electronic, Electronical, and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, P.R.China;

Corresponding?author：

XU Chundong, Email: xuchundong@jxust.edu.cn

Keywords：

heart sound denoising; improved minimum control recursive average; optimally modified log-spectral amplitude; noise estimate; heart sound classification

DOI：

10.7507/1001-5515.202002023

Video：

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Abstract Full text Figures/Tables Video References Cited by

Denoising methods based on wavelet analysis and empirical mode decomposition cannot essentially track and eliminate noise, which usually cause distortion of heart sounds. Based on this problem, a heart sound denoising method based on improved minimum control recursive average and optimally modified log-spectral amplitude is proposed in this paper. The proposed method uses a short-time window to smoothly and dynamically track and estimate the minimum noise value. The noise estimation results are used to obtain the optimal spectrum gain function, and to minimize the noise by minimizing the difference between the clean heart sound and the estimated clean heart sound. In addition, combined with the subjective analysis of spectrum and the objective analysis of contribution to normal and abnormal heart sound classification system, we propose a more rigorous evaluation mechanism. The experimental results show that the proposed method effectively improves the time-frequency features, and obtains higher scores in the normal and abnormal heart sound classification systems. The proposed method can help medical workers to improve the accuracy of their diagnosis, and also has great reference value for the construction and application of computer-aided diagnosis system.

Citation： XU Chundong, ZHOU Jing, YING Dongwen, XIN Pengli. Heart sound denoising by dynamic noise estimation. Journal of Biomedical Engineering, 2020, 37(5): 775-785. doi: 10.7507/1001-5515.202002023 Copy

1.	Zhang Wenjie, Han Jiqing, Deng Shiwen. Heart sound classification based on scaled spectrogram and partial least squares regression. Biomed Signal Process Control, 2017, 32: 20-28.
2.	Raza A, Mehmood A, Ullah S, et al. Heartbeat sound signal classification using deep learning. Sensors (Basel), 2019, 19(21): 4819.
3.	Puneet K J, Tiwari A K. An adaptive thresholding method for the wavelet based denoising of phonocardiogram signal. Biomed Signal Process Control, 2017, 38: 388-399.
4.	Semmlow J L. Improved heart sound detection and signal-to-noise estimation using a low-mass sensor. IEEE Transactions on Biomedical Engineering, 2016, 63(3): 647-652.
5.	董利超, 郭興明, 鄭伊能. 基于CEEMD的心音信號小波包去噪算法研究. 振動與沖擊, 2019, 38(9): 192-198, 222.
6.	Cheng Xiefeng, Zheng Zhang. Denoising method of heart sound signals based on self-construct heart sound wavelet. AIP Adv, 2014, 4(8): 87-108.
7.	Boutana D, Benidir M, Barkat B. On the selection of intrinsic mode function in EMD method: application on heart sound signal//2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010), Rome: IEEE, 2010: 1-5.
8.	Zaid A, Ebrahim A N, Manal A W, et al. Classification of heart sounds using fractional Fourier transform based Mel-frequency spectral coefficients and stacked autoencoder deep neural network. J Med Imaging Health Inform, 2019, 9(1): 1-8.
9.	Oliveira J, Renna F, Coimbra M. A Subject-Driven unsupervised hidden Semi-Markov model and gaussian mixture model for heart sound segmentation. IEEE J Sel Top Signal Process, 2019, 13(2): 323-331.
10.	劉俏俏, 譚志向, 張懿, 等. 基于經驗模態分解的胎心音信號除噪方法. 生物醫學工程學雜志, 2015, 32(4): 740-745, 772.
11.	王娜. 基于小波變換與約束方差噪聲譜估計的語音增強算法研究. 秦皇島: 燕山大學, 2011.
12.	Brunetti N D, Rosania S, D'antuono C, et al. Diagnostic accuracy of heart murmur in newborns with suspected congenital heart disease. J Cardiovasc Med (Hagerstown), 2015, 16(8): 556-561.
13.	Premkumar P. Utility of echocardiogram in the evaluation of heart murmurs. Med Clin North Am, 2016, 100(5): 991-1001.
14.	Whitaker B M, Suresha P B, Liu C, et al. Combining sparse coding and time-domain features for heart sound classification. Physiol Meas, 2017, 38(8): 1701-1713.
15.	Chen Lingguang, Wu F S, Xu Yong,, et al. Blind separation of heart sounds. Journal of the Acoustical Society of America, 2017, 137(04): 2388-2388.
16.	許春冬, 周靜, 龍清華, 等. 基于coif-5小波的心音自適應閾值降噪方法. 科學技術與工程, 2019, 19(2): 106-113.
17.	Yuan Wenhao, Lin Jiajun, Wei A N, et al. Noise estimation based on time–frequency correlation for speech enhancement. Applied Acoustics, 2013, 74(5): 770-781.
18.	Hirszhorn A, Dov D, Talmon R, et al. Transient interference suppression in speech signals based on the OM-LSA algorithm//International Workshop on Acoustic Signal Enhancement, Aachen: VDE, 2012: 1-4.
19.	袁文浩, 林家駿, 王雨, 等. 一種基于噪聲分類的語音增強方法. 華東理工大學學報:自然科學版, 2014, 40(2): 196-201.
20.	曹克宇. 語音系統中瞬態噪聲抑制算法的研究. 哈爾濱: 哈爾濱工業大學, 2017.
21.	陳成斌. 針對于家居環境的語音增強系統的研究與開發. 廣州: 華南理工大學, 2015.
22.	劉鳳增. 復雜環境下語音增強方法研究. 長沙: 國防科學技術大學, 2011.
23.	李國正, 譚南林, 蘇樹強, 等. 混沌同步系統降噪方法及其在滾動軸承故障診斷中的應用. 北京理工大學學報, 2019, 39(7): 669-675.
24.	Cohen I, Berdugo B. Speech enhancement for non-stationary noise environments. Signal Processing, 2001, 81(11): 2403-2418.
25.	Cohen I, Berdugo B. Spectral enhancement by tracking speech presence probability in subbands//IEEE Workshop on Hands-Free Speech Communication, Kyoto: IEEE, 2001: 95-98.
26.	Tran T D, Nguyen Q C, Nguyen D K. Speech enhancement using modified IMCRA and OMLSA methods//International Conference on Communications and Electronics 2010, Nha Trang: IEEE, 2010: 195-200.
27.	Ephraim H, Malah D. Speech enhancement using a minimum mean square error short-time spectral amplitude estimator. IEEE Transactions on Acoustics, Speech and Signal Processing, 1984, 32(06): 1109-1121.
28.	Hou Xuchu, Guo S, Cui H, et al. Speech enhancement for Non-Stationary noise environments//2009 International Conference on Information Engineering and Computer Science, Wuhan: IEEE, 2009: 1-3.
29.	Cohen I. Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging. IEEE Transactions on Speech and Audio Processing, 2003, 11(5): 466-475.
30.	熊晶. 語音增強中噪聲估計的研究. 蘭州: 蘭州交通大學, 2015.
31.	Clifford G, Liu Chengyu, Moody B, et al. Classification of normal/abnormal heart sound recordings: the PhysioNet/computing in cardiology challenge 2016//2016 Computing in Cardiology Conference (CinC), 2016: 609-612.
32.	Liu C Y, Springer D, Li Q, et al. An open access database for the evaluation of heart sound algorithms. Physiol Meas, 2016, 37(12): 2181-2213.
33.	成謝鋒, 陳亞敏. S1和S2共振峰頻率在心音分類識別中的應用. 南京郵電大學學報:自然科學版, 2017, 37(5): 7-12.
34.	陳亞敏. 一種基于級聯無損聲管的心音產生模型的研究及應用. 南京: 南京郵電大學, 2017.
35.	Hong Tang, Chen Huaming, Li Ting, et al. Classification of normal/abnormal heart sound recordings based on multi-domain features and back propagation neural network//2016 Computing in Cardiology Conference (CinC), Vancouver: IEEE, 2016: 593-596.
36.	Springer D B, Tarassenko L, Clifford G D. Logistic Regression-HSMM-Based heart sound segmentation. IEEE Trans Biomed Eng, 2016, 63(4): 822-832.
37.	Bouril A, Aleinikava D, Guillem M S, et al. Automated classification of normal and abnormal heart sounds using support vector machines//2016 Computing in Cardiology Conference (CinC), Vancouver: IEEE, 2016: 549-552.

1. Zhang Wenjie, Han Jiqing, Deng Shiwen. Heart sound classification based on scaled spectrogram and partial least squares regression. Biomed Signal Process Control, 2017, 32: 20-28.
2. Raza A, Mehmood A, Ullah S, et al. Heartbeat sound signal classification using deep learning. Sensors (Basel), 2019, 19(21): 4819.
3. Puneet K J, Tiwari A K. An adaptive thresholding method for the wavelet based denoising of phonocardiogram signal. Biomed Signal Process Control, 2017, 38: 388-399.
4. Semmlow J L. Improved heart sound detection and signal-to-noise estimation using a low-mass sensor. IEEE Transactions on Biomedical Engineering, 2016, 63(3): 647-652.
5. 董利超, 郭興明, 鄭伊能. 基于CEEMD的心音信號小波包去噪算法研究. 振動與沖擊, 2019, 38(9): 192-198, 222.
6. Cheng Xiefeng, Zheng Zhang. Denoising method of heart sound signals based on self-construct heart sound wavelet. AIP Adv, 2014, 4(8): 87-108.
7. Boutana D, Benidir M, Barkat B. On the selection of intrinsic mode function in EMD method: application on heart sound signal//2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010), Rome: IEEE, 2010: 1-5.
8. Zaid A, Ebrahim A N, Manal A W, et al. Classification of heart sounds using fractional Fourier transform based Mel-frequency spectral coefficients and stacked autoencoder deep neural network. J Med Imaging Health Inform, 2019, 9(1): 1-8.
9. Oliveira J, Renna F, Coimbra M. A Subject-Driven unsupervised hidden Semi-Markov model and gaussian mixture model for heart sound segmentation. IEEE J Sel Top Signal Process, 2019, 13(2): 323-331.
10. 劉俏俏, 譚志向, 張懿, 等. 基于經驗模態分解的胎心音信號除噪方法. 生物醫學工程學雜志, 2015, 32(4): 740-745, 772.
11. 王娜. 基于小波變換與約束方差噪聲譜估計的語音增強算法研究. 秦皇島: 燕山大學, 2011.
12. Brunetti N D, Rosania S, D'antuono C, et al. Diagnostic accuracy of heart murmur in newborns with suspected congenital heart disease. J Cardiovasc Med (Hagerstown), 2015, 16(8): 556-561.
13. Premkumar P. Utility of echocardiogram in the evaluation of heart murmurs. Med Clin North Am, 2016, 100(5): 991-1001.
14. Whitaker B M, Suresha P B, Liu C, et al. Combining sparse coding and time-domain features for heart sound classification. Physiol Meas, 2017, 38(8): 1701-1713.
15. Chen Lingguang, Wu F S, Xu Yong,, et al. Blind separation of heart sounds. Journal of the Acoustical Society of America, 2017, 137(04): 2388-2388.
16. 許春冬, 周靜, 龍清華, 等. 基于coif-5小波的心音自適應閾值降噪方法. 科學技術與工程, 2019, 19(2): 106-113.
17. Yuan Wenhao, Lin Jiajun, Wei A N, et al. Noise estimation based on time–frequency correlation for speech enhancement. Applied Acoustics, 2013, 74(5): 770-781.
18. Hirszhorn A, Dov D, Talmon R, et al. Transient interference suppression in speech signals based on the OM-LSA algorithm//International Workshop on Acoustic Signal Enhancement, Aachen: VDE, 2012: 1-4.
19. 袁文浩, 林家駿, 王雨, 等. 一種基于噪聲分類的語音增強方法. 華東理工大學學報:自然科學版, 2014, 40(2): 196-201.
20. 曹克宇. 語音系統中瞬態噪聲抑制算法的研究. 哈爾濱: 哈爾濱工業大學, 2017.
21. 陳成斌. 針對于家居環境的語音增強系統的研究與開發. 廣州: 華南理工大學, 2015.
22. 劉鳳增. 復雜環境下語音增強方法研究. 長沙: 國防科學技術大學, 2011.
23. 李國正, 譚南林, 蘇樹強, 等. 混沌同步系統降噪方法及其在滾動軸承故障診斷中的應用. 北京理工大學學報, 2019, 39(7): 669-675.
24. Cohen I, Berdugo B. Speech enhancement for non-stationary noise environments. Signal Processing, 2001, 81(11): 2403-2418.
25. Cohen I, Berdugo B. Spectral enhancement by tracking speech presence probability in subbands//IEEE Workshop on Hands-Free Speech Communication, Kyoto: IEEE, 2001: 95-98.
26. Tran T D, Nguyen Q C, Nguyen D K. Speech enhancement using modified IMCRA and OMLSA methods//International Conference on Communications and Electronics 2010, Nha Trang: IEEE, 2010: 195-200.
27. Ephraim H, Malah D. Speech enhancement using a minimum mean square error short-time spectral amplitude estimator. IEEE Transactions on Acoustics, Speech and Signal Processing, 1984, 32(06): 1109-1121.
28. Hou Xuchu, Guo S, Cui H, et al. Speech enhancement for Non-Stationary noise environments//2009 International Conference on Information Engineering and Computer Science, Wuhan: IEEE, 2009: 1-3.
29. Cohen I. Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging. IEEE Transactions on Speech and Audio Processing, 2003, 11(5): 466-475.
30. 熊晶. 語音增強中噪聲估計的研究. 蘭州: 蘭州交通大學, 2015.
31. Clifford G, Liu Chengyu, Moody B, et al. Classification of normal/abnormal heart sound recordings: the PhysioNet/computing in cardiology challenge 2016//2016 Computing in Cardiology Conference (CinC), 2016: 609-612.
32. Liu C Y, Springer D, Li Q, et al. An open access database for the evaluation of heart sound algorithms. Physiol Meas, 2016, 37(12): 2181-2213.
33. 成謝鋒, 陳亞敏. S1和S2共振峰頻率在心音分類識別中的應用. 南京郵電大學學報:自然科學版, 2017, 37(5): 7-12.
34. 陳亞敏. 一種基于級聯無損聲管的心音產生模型的研究及應用. 南京: 南京郵電大學, 2017.
35. Hong Tang, Chen Huaming, Li Ting, et al. Classification of normal/abnormal heart sound recordings based on multi-domain features and back propagation neural network//2016 Computing in Cardiology Conference (CinC), Vancouver: IEEE, 2016: 593-596.
36. Springer D B, Tarassenko L, Clifford G D. Logistic Regression-HSMM-Based heart sound segmentation. IEEE Trans Biomed Eng, 2016, 63(4): 822-832.
37. Bouril A, Aleinikava D, Guillem M S, et al. Automated classification of normal and abnormal heart sounds using support vector machines//2016 Computing in Cardiology Conference (CinC), Vancouver: IEEE, 2016: 549-552.

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