TY - JOUR
T1 - Deep learning for ECG Arrhythmia detection and classification
T2 - an overview of progress for period 2017–2023
AU - Ansari, Yaqoob
AU - Mourad, Omar
AU - Qaraqe, Khalid
AU - Serpedin, Erchin
N1 - Publisher Copyright:
Copyright © 2023 Ansari, Mourad, Qaraqe and Serpedin.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - anomaly
KW - arrhythmia detection
KW - cardiology
KW - cardiovascular diseases
KW - deep learning
KW - electrocardiography
UR - http://www.scopus.com/inward/record.url?scp=85173091483&partnerID=8YFLogxK
U2 - 10.3389/fphys.2023.1246746
DO - 10.3389/fphys.2023.1246746
M3 - Review article
AN - SCOPUS:85173091483
SN - 1664-042X
VL - 14
JO - Frontiers in Physiology
JF - Frontiers in Physiology
M1 - 1246746
ER -