A heart sound classification method based on complete ensemble empirical modal decomposition with adaptive noise permutation entropy and support vector machine_Journal of Biomedical Engineering

Authors：

LIU Meijun ,  WU Quanyu , DING Sheng , PAN Lingjiao , LIU Xiaojie

Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, P. R. China;

Corresponding?author：

WU Quanyu, Email: wuquanyu@jsut.edu.cn

Keywords：

Heart sound classification; Complete ensemble empirical modal decomposition with adaptive noise; Permutation entropy; Support vector machine

DOI：

10.7507/1001-5515.202105065

Video：

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Heart sound signal is a kind of physiological signal with nonlinear and nonstationary features. In order to improve the accuracy and efficiency of the phonocardiogram (PCG) classification, a new method was proposed by means of support vector machine (SVM) in which the complete ensemble empirical modal decomposition with adaptive noise (CEEMDAN) permutation entropy was as the eigenvector of heart sound signal. Firstly, the PCG was decomposed by CEEMDAN into a number of intrinsic mode functions (IMFs) from high to low frequency. Secondly, the IMFs were sifted according to the correlation coefficient, energy factor and signal-to-noise ratio. Then the instantaneous frequency was extracted by Hilbert transform, and its permutation entropy was constituted into eigenvector. Finally, the accuracy of the method was verified by using a hundred PCG samples selected from the 2016 PhysioNet/CinC Challenge. The results showed that the accuracy rate of the proposed method could reach up to 87%. In comparison with the traditional EMD and EEMD permutation entropy methods, the accuracy rate was increased by 18%–24%, which demonstrates the efficiency of the proposed method.

Citation： LIU Meijun, WU Quanyu, DING Sheng, PAN Lingjiao, LIU Xiaojie. A heart sound classification method based on complete ensemble empirical modal decomposition with adaptive noise permutation entropy and support vector machine. Journal of Biomedical Engineering, 2022, 39(2): 311-319. doi: 10.7507/1001-5515.202105065 Copy

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18.	吳全玉, 張文強, 潘玲佼, 等. 一種結合自適應噪聲完備經驗模態分解和盲反卷積去除腦電中眼電偽跡的新方法. 數據采集與處理, 2020, 35(4): 720-729.
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21.	Wu Z H, Huang N E. Ensemble empirical mode decomposition: a noise assisted data analysis method. AADA, 2009(1): 1-41.
22.	Yeh J R, Shieh J S, Huang N E. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. AADA, 2011, 2(2): 135-156.
23.	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.
24.	Torres M E, Colominas M A, Schlotthauer G, et al. A complete ensemble empirical mode decomposition with adaptive noise// IEEE International Conference on Acoustics. Prague: IEEE, 2011: 4144-4147.
25.	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.
26.	Clifford G D, Liu C Y, Moody B, et al. Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in Cardiology Challenge 2016// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2017: 609-612.

1. 馬晶, 蔡文杰, 楊利. 心音信號分析. 中國醫學物理學雜志, 2017, 34(11): 72-75.
2. Singh-Miller N E, Singh-Miller N. Using spectral acoustic features to identify abnormal heart sounds// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2017: 557-560.
3. 許春冬, 周靜, 應冬文, 等. 心音信號MFCC特征向量提取方法的優化. 信號處理, 2019, 35(3): 410-418.
4. 邢素霞, 陳天華. 基于DSP的心音信號采集與分析. 生物醫學工程學雜志, 2011, 28(2): 273-276.
5. Zabihi M, Rad A B, Kiranyaz S, et al. Heart sound anomaly and quality detection using ensemble of neural networks without segmentation// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2017: 613-616.
6. Nogueira D M, Ferreira C A, Gomes E F, et al. Classifying heart sounds using images of motifs, MFCC and temporal features. J Med Syst, 2019, 43(6): 168.
7. Ortiz J J G, Phoo C P, Wiens J. Heart sound classification based on temporal alignment techniques// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2017: 589-592.
8. 張小蘭, 房玉, 劉棟博, 等. 肥心病心音時頻雜波特征提取識別算法研究. 電子測量與儀器學報, 2020, 34(4): 20-26.
9. 郭春璐, 岳小冰. 基于支持向量機的心音信號自動識別. 計算機與現代化, 2016(6): 36-39.
10. 全雪峰. 基于S變換和局部奇異熵的心音信號識別. 電子技術與軟件工程, 2015(15): 260-264.
11. 劉麗萍, 袁剛. 心音信號的特征分析及分類識別. 信息通信, 2016(10): 26-29.
12. 尹明, 王燕, 劉欣. 心音的降噪預處理和基于HHT的特征分析研究. 電聲技術, 2013, 37(6): 58-63.
13. 黃林洲, 郭興明, 丁曉蓉. EMD近似熵結合支持向量機的心音信號識別研究. 振動與沖擊, 2012, 31(19): 21-25.
14. 李宏全, 郭興明, 鄭伊能. 基于EMD和MFCC的舒張期心雜音的分類識別. 振動與沖擊, 2017, 36(11): 8-13.
15. Goda M A, Hajas P. Morphological determination of pathological PCG signals by time and frequency domain analysis// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2017: 1133-1136.
16. Grzegorczyk I, Katarzyna S, Gieratowski J, et al. PCG classification using a neural network approach// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2017: 1129-1132.
17. Langley P, Murray A. Abnormal heart sounds detected from short duration unsegmented phonocardiograms by wavelet entropy// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2017: 545-548.
18. 吳全玉, 張文強, 潘玲佼, 等. 一種結合自適應噪聲完備經驗模態分解和盲反卷積去除腦電中眼電偽跡的新方法. 數據采集與處理, 2020, 35(4): 720-729.
19. Zheng Y N, Guo X M, Qin J, et al. Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics. Comput Methods Programs Biomed, 2015, 122(3): 372-383.
20. Vernekar S, Nair S, Vijayasenan D, et al. A novel approach for classification of normal/abnormal phonocardiogram recordings using temporal signal analysis and machine learning// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2017: 1141-1144.
21. Wu Z H, Huang N E. Ensemble empirical mode decomposition: a noise assisted data analysis method. AADA, 2009(1): 1-41.
22. Yeh J R, Shieh J S, Huang N E. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. AADA, 2011, 2(2): 135-156.
23. 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.
24. Torres M E, Colominas M A, Schlotthauer G, et al. A complete ensemble empirical mode decomposition with adaptive noise// IEEE International Conference on Acoustics. Prague: IEEE, 2011: 4144-4147.
25. 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.
26. Clifford G D, Liu C Y, Moody B, et al. Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in Cardiology Challenge 2016// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2017: 609-612.

Journal of Biomedical Engineering

A heart sound classification method based on complete ensemble empirical modal decomposition with adaptive noise permutation entropy and support vector machine

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