A heart sound classification method based on complete ensemble empirical modal decomposition with adaptive noise permutation entropy and support vector machine / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 311-319, 2022.
Artículo
en Chino
| WPRIM
| ID: wpr-928227
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.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Procesamiento de Señales Asistido por Computador
/
Ruidos Cardíacos
/
Entropía
/
Relación Señal-Ruido
/
Máquina de Vectores de Soporte
Idioma:
Chino
Revista:
Journal of Biomedical Engineering
Año:
2022
Tipo del documento:
Artículo
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