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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21.	Wu Z H, Huang N E. Ensemble empirical mode decomposition: a noise assisted data analysis method. AADA, 2009(1): 1-41.
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23.	董利超, 郭兴明, 郑伊能. 基于CEEMD的心音信号小波包去噪算法研究. 振动与冲击, 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. 马晶, 蔡文杰, 杨利. 心音信号分析. 中国医学物理学杂志, 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. 许春冬, 周静, 应冬文, 等. 心音信号MFCC特征向量提取方法的优化. 信号处理, 2019, 35(3): 410-418.
4. 邢素霞, 陈天华. 基于DSP的心音信号采集与分析. 生物医学工程学杂志, 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. 张小兰, 房玉, 刘栋博, 等. 肥心病心音时频杂波特征提取识别算法研究. 电子测量与仪器学报, 2020, 34(4): 20-26.
9. 郭春璐, 岳小冰. 基于支持向量机的心音信号自动识别. 计算机与现代化, 2016(6): 36-39.
10. 全雪峰. 基于S变换和局部奇异熵的心音信号识别. 电子技术与软件工程, 2015(15): 260-264.
11. 刘丽萍, 袁刚. 心音信号的特征分析及分类识别. 信息通信, 2016(10): 26-29.
12. 尹明, 王燕, 刘欣. 心音的降噪预处理和基于HHT的特征分析研究. 电声技术, 2013, 37(6): 58-63.
13. 黄林洲, 郭兴明, 丁晓蓉. EMD近似熵结合支持向量机的心音信号识别研究. 振动与冲击, 2012, 31(19): 21-25.
14. 李宏全, 郭兴明, 郑伊能. 基于EMD和MFCC的舒张期心杂音的分类识别. 振动与冲击, 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. 吴全玉, 张文强, 潘玲佼, 等. 一种结合自适应噪声完备经验模态分解和盲反卷积去除脑电中眼电伪迹的新方法. 数据采集与处理, 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, 郭兴明, 郑伊能. 基于CEEMD的心音信号小波包去噪算法研究. 振动与冲击, 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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