Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss.
Math Biosci Eng
; 21(4): 5521-5535, 2024 Mar 22.
Article
em En
| MEDLINE
| ID: mdl-38872546
ABSTRACT
Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification. Furthermore, a global average RR interval was introduced to address the issue of similarities between N vs. S categories, and a weighted loss function was developed to solve the imbalance problem using the dynamically adjusted weights based on the proportion of each class in the input batch. The MIT-BIH arrhythmia dataset was used to validate the classification performances of the proposed method. Experimental results under the intra-patient evaluation paradigm and inter-patient evaluation paradigm showed that the proposed method could achieve better classification results than other methods. Among them, the accuracy, average sensitivity, average precision, and average specificity under the intra-patient paradigm were 98.73%, 94.89%, 89.38%, and 98.24%, respectively. The accuracy, average sensitivity, average precision, and average specificity under the inter-patient paradigm were 91.22%, 89.91%, 68.23%, and 95.23%, respectively.
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Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Arritmias Cardíacas
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Algoritmos
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Processamento de Sinais Assistido por Computador
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Redes Neurais de Computação
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Eletrocardiografia
Limite:
Humans
Idioma:
En
Revista:
Math Biosci Eng
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China