A Systematic Review of Deep Learning Techniques for ECG Arrhythmia Classification

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Dipanjan Dutta; Anusudha.K

A Systematic Review of Deep Learning Techniques for ECG Arrhythmia Classification

Authors: Dipanjan Dutta, Anusudha.K

Unique Paper ID: 170692
Volume: 11
Issue: 7
PageNo: 515-524

Keywords: ECG diagnostics clinical decision support CNNs DBM hybrid DL RNNs including LSTM and BiLSTM GRU.

Abstract:
The use of Deep Learning (DL) methods for detecting arrhythmias from ECG data, focusing on their clinical potential while highlighting areas that need more research for reliable application. The study stresses the importance of using diverse ECG datasets, advanced noise reduction, data augmentation, and new integrated DL models for better accuracy in clinical settings. Specifically, the authors introduce a hybrid model that combines Convolutional Neural Networks (CNN) with Variational Autoencoders (VAE) — the first use of VAE for classifying 1-D ECG signals. This model uses both supervised and unsupervised learning and shows better performance compared to standard CNNs and Autoencoders (AEs), particularly in reducing errors in diagnosing different cardiovascular diseases (CVDs). The paper also addresses challenges like data imbalance, which can be improved by using real-time data and augmentation techniques, thereby increasing model accuracy. Furthermore, In some paper we come across developing a mobile app based on this CNN-VAE model, which could help healthcare professionals predict high-risk CVD patients. The review also covers successful DL algorithms, such as GRU, LSTM, DBM, BiLSTM and CNN, which have proven effective in classifying arrhythmias like atrial fibrillation and ventricular ectopic beats. The study highlights the importance of selecting the right DL algorithm to achieve optimal classification results.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 7
PageNo: 515-524

A Systematic Review of Deep Learning Techniques for ECG Arrhythmia Classification

Available:https://ijirt.org/article?manuscript=170692

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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