Transcending Boundaries: Assessing Transfer Learning’s Effectiveness in ECG-Based Heart Disease Prediction

Medical studies concentrate a significant amount of focus on heart disease due to its substantial impact on human health. Cardiovascular diseases, such as myocardial infarctions, cerebrovascular accidents, and cardiac dysrhythmias, arise from any deviation in the normal functioning of the heart. Cardiovascular disorders can be effectively treated using automated systems for early diagnosis and treatment of cardiovascular conditions. When the sinoatrial node in the heart generates an abnormal electrical signal or improperly transmits that signal, it results in an erratic heartbeat, which is also referred to as cardiac arrhythmia. Non-invasive electrocardiograms (ECGs) can identify these anomalies. ECGs are used to detect abnormal heart rhythms, assess the variability of heart rate, predict the occurrence of heart attacks, and evaluate the ability of the heart to contract. This chapter aims to provide a thorough comprehension of the process of diagnosing cardiovascular problems through the analysis of the ECG signal. This chapter encompasses the essential concepts and techniques that have been used in recent years, together with a systematic approach based on machine learning (ML) and deep learning (DL). Based on recent research, it is evident that the use of ML and DL approaches has led to a significant improvement in the accuracy of categorizing cardiac arrhythmia. The highly advanced and optimized deep convolutional technology known as FT-DCNN has an impressive accuracy of 99.56% in recognizing cardiovascular abnormalities. In comparison, transfer learning (TL) achieves an F-1 score of 99.44%, outperforming other methods.