Heart Sound Classification Method Based on Complete Empirical Mode Decomposition with Adaptive Noise Permutation Entropy

This paper proposes a method of complete empirical modal decomposition with adaptive noise (CEEMDAN) arrangement entropy as a characteristic vector of heart sound signal and support vector machine (SVM) as a classifier to classify heart sounds. Firstly, PCG is decomposed into a few intrinsic mode fu...

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Bibliographic Details
Published in:Journal of physics. Conference series 2022-01, Vol.2173 (1), p.12018
Main Authors: Wu, Quanyu, Liu, Meijun, Ding, Sheng, Pan, Lingjiao, Liu, Xiaojie
Format: Article
Language:English
Subjects:
Acoustics
Classification
Correlation coefficients
Feature extraction
Heart
Hilbert transformation
Mathematical analysis
Noise
Permutations
Physics
Signal to noise ratio
Sound
Support vector machines
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Summary:This paper proposes a method of complete empirical modal decomposition with adaptive noise (CEEMDAN) arrangement entropy as a characteristic vector of heart sound signal and support vector machine (SVM) as a classifier to classify heart sounds. Firstly, PCG is decomposed into a few intrinsic mode functions (IMF) from high frequency to low frequency with CEEMDAN. Secondly, this method uses the correlation coefficient, energy factor and signal-to-noise ratio of the IMF and the original signal to optimize IMFs for Hilbert transform getting instantaneous frequency. Making use of the instantaneous frequency calculates arrangement entropy of each IMF. These arrangement entropy values form a feature vector. Finally, the extracted features were classified by help of support vector machine (SVM) with the mark of normal and abnormal heart sounds. The 100 heart sound samples from the 2016 PhysioNet /CinC Challenge were classified between normal and abnormal, and experimental results show that this method can effectively improve the recognition accuracy. The classification results of support vector machine reach 87%, and are better than Fisher discrimination methods.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2173/1/012018