Deep Learning Architectures for ECG Classification in Cardiac Arrhythmia Detection: A review
DOI:
https://doi.org/10.21834/e-bpj.v10iSI32.7056Keywords:
Arrythmia detection, Deep learning models, ECG ClassificationAbstract
Cardiac arrhythmias are irregular heartbeats that may cause severe complications, including stroke and heart failure. Early detection is essential for diagnosis and treatment. This study examines deep learning models for ECG classification, evaluating CNNs, RNNs, and LSTMs. Results indicate LSTMs achieve the highest accuracy: 97.3% (PTB), 93.11% (ECG-ID), and 96.81% (MIT-BIH). Hybrid CNN-LSTM models enhance performance, particularly on imbalanced datasets. These findings demonstrate deep learning’s effectiveness, especially LSTMs, in improving ECG classification. Enhanced accuracy in arrhythmia detection can support cardiologists in diagnosing conditions more efficiently, leading to better patient outcomes and automated diagnostic advancements.
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Copyright (c) 2025 Nur Amelia Natasha Abdul Rofar, Ziti Fariha Mohd Apandi, Nur Sukinah Aziz, Wan Roslina Wan Othman
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
