An Exhaustive Survey of Deep Learning Techniques in ECG Signals

Abstract

     The electrocardiogram (ECG) is a widely utilized signal in the prediction of Cardiovascular Diseases (CVDs). ECG signals have the ability to detect the heart's unusual rhythmic patterns, which are generally referred to as arrhythmias. A comprehensive examination of ECG signals is crucial for the precise identification of patients' chronic heart conditions. This literature survey investigates some of the most recent studies in cardiac research where researchers embarked on the arduous task of creating an ECG signal image model to identify possible signals signifying the presence of heart disease. This survey covers algorithms for fusing ECG signal features with deep learning methods to develop deep neural networks for predicting heart disease using images obtained from recordings of ECG signals. Respectively, every study provides an aspect of understanding how complicated Deep Learning (DL) models can find undiscovered elements from ECG data that lead to earlier and correct diagnoses of cardiovascular disorders in this objective of the discussed project. Through this journey through these research findings, we will look closely at the methods used and draw conclusions on how DL models can be improved in the context of cardiac health. This paper endeavors to introduce novelty into this developing field and thus forms part of this extensive review of what is currently available in this area.