GAF-GradCAM: Guided dynamic weighted fusion of temporal and frequency GAF 2D matrices for ECG-based arrhythmia detection using deep learning

GAF-GradCAM: Guided dynamic weighted fusion of temporal and frequency GAF 2D matrices for ECG-based arrhythmia detection using deep learning

This study introduces an innovative approach for arrhythmia classification that employs a Grad-CAM-guided dynamic weighted fusion of temporal and frequency features extracted from electrocardiogram (ECG) signals. By transforming ECG signals into two-dimensional Gramian Angular Field (GAF) matrices,...

Full description

Saved in:
Bibliographic Details
Main Authors: Zakaria Khatar, Dounia Bentaleb, Noreddine Abghour, Khalid Moussaid
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Scientific African
Subjects:
ECG signal analysis
Gramian Angular Field
Grad-CAM
Dynamic fusion
Arrhythmia classification
Deep learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2468227625001577
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S2468227625001577

Similar Items