SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural network

SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural network

<p dir="ltr">Electrocardiogram (ECG) signals play a critical role in diagnosing cardiovascular diseases (CVDs), yet automated ECG classification remains challenging due to inter-patient variability, signal noise, and heart rhythm complexity. To address these challenges, we propose a...

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Bibliographic Details
Main Author: Samin Yaser (22803530) (author)
Other Authors: Muhammad E.H. Chowdhury (17151154) (author), Rusab Sarmun (17632269) (author)
Published: 2025
Subjects:
Biomedical and clinical sciences
Cardiovascular medicine and haematology
Engineering
Biomedical engineering
Health sciences
Health services and systems
Information and computing sciences
Artificial intelligence
Machine learning
Cardiac arrhythmia
Deep learning
Residual Temporal Convolutional Network
Feature map
Graph neural network
Signal processing
Electrocardiogram (ECG)
Classification model
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