Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023

Frontiers in Physiology

Physiology

Erchin Serpedin¹  Yaqoob Ansari²  Khalid Qaraqe²  Omar Mourad³ 

[1] ECEN Department, Texas A&M University, College Station, TX, United States;ECEN Program, Texas A&M University at Qatar, Doha, Qatar;Weill Cornell Medicine-Qatar, Doha, Qatar;

关键词: cardiovascular diseases;    arrhythmia detection;    deep learning;    electrocardiography;    cardiology;    anomaly;   

DOI  :  10.3389/fphys.2023.1246746

 received in 2023-06-26, accepted in 2023-08-28,  发布年份 2023

来源: Frontiers

【 摘 要 】

Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography (ECG) still represents the benchmark approach for identifying cardiac irregularities. Automatic detection of abnormalities from the ECG can aid in the early detection, diagnosis, and prevention of cardiovascular diseases. Deep Learning (DL) architectures have been successfully employed for arrhythmia detection and classification and offered superior performance to traditional shallow Machine Learning (ML) approaches. This survey categorizes and compares the DL architectures used in ECG arrhythmia detection from 2017–2023 that have exhibited superior performance. Different DL models such as Convolutional Neural Networks (CNNs), Multilayer Perceptrons (MLPs), Transformers, and Recurrent Neural Networks (RNNs) are reviewed, and a summary of their effectiveness is provided. This survey provides a comprehensive roadmap to expedite the acclimation process for emerging researchers willing to develop efficient algorithms for detecting ECG anomalies using DL models. Our tailored guidelines bridge the knowledge gap allowing newcomers to align smoothly with the prevailing research trends in ECG arrhythmia detection. We shed light on potential areas for future research and refinement in model development and optimization, intending to stimulate advancement in ECG arrhythmia detection and classification.

【 授权许可】

Unknown   
Copyright © 2023 Ansari, Mourad, Qaraqe and Serpedin.