Why the study?
Deep learning has emerged in healthcare for timely detection of ECG anomalies in patient monitoring, prompting a comprehensive review of recent deep learning methods for ECG classification.
Do deep learning methods accurately classify arrhythmias on ECG?
Do deep learning methods accurately classify arrhythmias on ECG?
Deep learning methods, particularly CNNs, demonstrate exceptionally high accuracy in classifying ECG arrhythmias such as AF, SVEB, and VEB.
DL methods may achieve high ECG arrhythmia classification accuracy; leaves open prospective validation before clinical adoption.
Deep Learning (DL) has recently become a topic of study in different applications including healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a vital role in patient monitoring. This paper presents a comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes. This study considers various types of the DL methods such as Convolutional Neural Network (CNN), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). From the 75 studies reported within 2017 and 2018, CNN is dominantly observed as the suitable technique for feature extraction, seen in 52% of the studies. DL methods showed high accuracy in correct classification of Atrial Fibrillation (AF) (100%), Supraventricular Ectopic Beats (SVEB) (99.8%), and Ventricular Ectopic Beats (VEB) (99.7%) using the GRU/LSTM, CNN, and LSTM, respectively.
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Ebrahimi et al. (2020) studied this question.
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