A Novel Deep 2D-CNN Model for ECG-Based Arrhythmia Diagnosis with Selective Attention Mechanism and CWT Integration

A Novel Deep 2D-CNN Model for ECG-Based Arrhythmia Diagnosis with Selective Attention Mechanism and CWT Integration

This study introduces an innovative approach for arrhythmia diagnosis via electrocardiogram (ECG) signals, employing a 2D Convolutional Neural Network (CNN) model fused with a Continuous Wavelet Transform (CWT) and a Selective Attention Mechanism (SAM). The SAM enhances feature focus, improving clas...

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Bibliographic Details
Main Authors: Hassanain Shakir Mansour, Morteza Valizadeh, Alaa Hussein Abdulaal, Mehdi Chehel Amirani
Format: Article
Language:English
Published: Faculty of Engineering, University of Kufa 2025-04-01
Series:Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ
Subjects:
arrhythmia
ecg signals
cnn
continuous wavelet transform
selective attention mechanism
Online Access:https://journal.uokufa.edu.iq/index.php/kje/article/view/17579
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https://journal.uokufa.edu.iq/index.php/kje/article/view/17579

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