Interpatient ECG Arrhythmia Detection by Residual Attention CNN.
Comput Math Methods Med
; 2022: 2323625, 2022.
Article
en En
| MEDLINE
| ID: mdl-35432590
ABSTRACT
The precise identification of arrhythmia is critical in electrocardiogram (ECG) research. Many automatic classification methods have been suggested so far. However, efficient and accurate classification is still a challenge due to the limited feature extraction and model generalization ability. We integrate attention mechanism and residual skip connection into the U-Net (RA-UNET); besides, a skip connection between the RA-UNET and a residual block is executed as a residual attention convolutional neural network (RA-CNN) for accurate classification. The model was evaluated using the MIT-BIH arrhythmia database and achieved an accuracy of 98.5% and F 1 scores for the classes S and V of 82.8% and 91.7%, respectively, which is far superior to other approaches.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Electrocardiografía
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Comput Math Methods Med
Asunto de la revista:
INFORMATICA MEDICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
China