Heartbeat classification method combining multi-branch convolutional neural networks and transformer.
iScience
; 27(3): 109307, 2024 Mar 15.
Article
em En
| MEDLINE
| ID: mdl-38482492
ABSTRACT
The detection and classification of arrhythmias are crucial steps in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often fail to consider both the morpho-logical and temporal features of the electrocardiogram (ECG) simultaneously. Therefore, we propose a hybrid heartbeat classification method that combines Transformer and multi branch convolutional neural networks (CNNs). Then, use the fusion module to stitch the features obtained from different classifiers. We performed three different heartbeat classification protocols on the MIT-BIH arrhythmia (MIT-BIH-AR) database and analyzed performance on SVEB and VEB classes to validate our method. The first was an intra-patient protocol with an overall accuracy of 99.5%, with 92.4% and 99.9% for Sen and Spe on SVEB and 98.2% and 99.9% for Sen and Spe on VEB. The latter two were inter-patient protocols, and we divided the training and test sets using different records, and the results showed an overall accuracy of 98.8% and 97.2%, respectively.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
IScience
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China