Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review

Abstract

Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average F1 score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities.

A hybrid deep learning model for multiclass arrhythmia detection and classification using photoplethysmography signals

“…The authors conducted a systematic analysis in [14] to analyse the ECG database, preprocessing techniques, DL method, assessment pattern, performance measure, and code accessibility to recognize research trends, problems, and possibilities for DL-based ECG AM classification. More precisely, a total of 368 research that match the specified criteria are included.…”

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

“…Previous literature such as Bizopoulos and Koutsouris (2018) , Dewangan and Shukla (2015) , Dinakarrao et al (2019) , and Luz et al (2016) have offered an overview of detection and classification methods for arrhythmia up to the year 2019. However, we identified a conspicuous void of comprehensive surveys enveloping the recent years, with only a few works like ( Parvaneh et al, 2019 ; Teplitzky et al, 2020 ; Xiao et al, 2023a ) not considering studies past the year 2022. These investigations have surveyed existing literature but need to improve their provision of in-depth comparative chronological analyses.…”

“…Recent years have seen reviews like ( Xiao et al, 2023a ), which map recent DL works (up to the year 2022) quite comprehensively. However, this review is not tailored for novice researchers and is an indiscriminate compilation of DL works with little intertextual summarization.…”

Combining the Taguchi Method and Convolutional Neural Networks for Arrhythmia Classification by Using ECG Images with Single Heartbeats

“…A deep residual network (ResNet) was presented for the classification of cardiac arrhythmias [49]. In 2023, a systematic review will be performed on the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification [50].…”