Enhanced Electrocardiogram Arrhythmia Diagnosis with Deep Learning and Selective Attention Mechanism
Year: 2025
Type: article
Abstract: The study aims to improve the diagnosis of arrhythmia in cardiovascular disease management. A novel approach using a deep convolutional network combined with a selective attention mechanism is proposed for electrocardiogram signal classification. The deep convolutional network extracts relevant features directly from raw electrocardiogram signals, while the selective attention mechanism focuses on the most critical regions of the signals and suppresses irrelevant or noisy components. This method achieves an accuracy of 99.70% in multi-class arrhythmia classification and 99.85% in binary classification, significantly outperforming traditional classification algorithms. Furthermore, the selective attention mechanism improves the localization of critical electrocardiogram segments, offering valuable insights for clinicians and aiding in the diagnosis process. This enhanced approach increases diagnostic accuracy and provides a clearer understanding of the electrocardiogram signals, which is crucial for effective patient management in cardiovascular diseases.
Authors: Hasanain Shakir Mansour, Morteza Valizadeh, Alaa Hussein Abdulaal, Mehdi Chehl Amirani
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4411615657