Enhanced Electrocardiogram Arrhythmia Diagnosis with Deep Learning and Selective Attention Mechanism

Authors

  • Hasanain Shakir Mansour Department of Computer Technical Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University, Al-Muthanna, Iraq/ Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran
  • Morteza Valizadeh Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran
  • Alaa Hussein Abdulaal Department of Electrical Engineering, College of Engineering, Al-Iraqia University, Baghdad, Iraq
  • Mehdi Chehl Amirani Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran

DOI:

https://doi.org/10.46604/aiti.2024.14034

Keywords:

arrhythmia diagnosis, electrocardiogram (ECG), deep convolutional network (DCNN), selective attention mechanism (SAM)

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.

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Published

2025-02-06

How to Cite

[1]
Hasanain Shakir Mansour, Morteza Valizadeh, Alaa Hussein Abdulaal, and Mehdi Chehl Amirani, “Enhanced Electrocardiogram Arrhythmia Diagnosis with Deep Learning and Selective Attention Mechanism”, Adv. technol. innov., Feb. 2025.

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