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

Meijun Liu; Quanyu WU; Sheng Ding; Lingjiao Pan; Xiaojie Liu

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

Author: Meijun LIU ¹ ; Quanyu WU ¹ ; Sheng DING ¹ ; Lingjiao PAN ¹ ; Xiaojie LIU ¹
Author Information

1. Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, P. R. China.
Publication Type:Journal Article
Keywords: Complete ensemble empirical modal decomposition with adaptive noise; Heart sound classification; Permutation entropy; Support vector machine
MeSH: Entropy; Heart Sounds; Signal Processing, Computer-Assisted; Signal-To-Noise Ratio; Support Vector Machine
From: Journal of Biomedical Engineering 2022;39(2):311-319
CountryChina
Language:Chinese
Abstract: 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.