Improving ECG signals classification by using deep learning techniques: A review

Abstract

Heart diseases are serious global health concerns that could result in many deaths. Detecting and classifying the heart diseases early is crucial for initiating treatment and improving patient outcomes. ECG signals contain valuable information to analyze cardiac functions. It can be argued that techniques of Deep learning (DL) are effective aid to classify ECG signals accurately through learning from large amount of ECG data, ability to extract hidden information, and achieving superior performance in detection heart abnormalities. ECG signals processing involves three phases, preprocessing, extraction features and classification. This paper intends to review several studies published from 2019 to 2024 in this field. It follows a method of comparative analysis, considering specific performance metrics, preprocessing techniques, and the DL model used. The aim is to determine the most accurate DL technique for classifying ECG signals. Eventually, the paper indicated that the debate on the most accurate technique for classification remains ongoing. However , the reviewed studies demonstrated that models based on CNN and RNN can achieve significant level of accuracy in classifying ECG signals. On other hand, according to the conducted comparative analysis, it is recommended to use VGG16 as a classifier for ECG signals. As a suggestion, the complexity of VGG16 can be reduced, allowing for the implementation of a real-time application.