Deep Learning Based Classification of Cardiovascular Diseases Via ECG Signal Analysis

This research study focuses on developing a deep learning algorithm to analyze ECG signals for the early detection of cardiovascular diseases, particularly heart arrhythmias and heart failure. The proposed study aims to design and implement a deep learning model to classify and screen ECG signals for abnormalities. The dataset consists of ECG signals from various databases, including those representing normal heart rhythms, arrhythmias, and congestive heart failure. The algorithm's performance is evaluated by comparing it with existing computer programs. The results are expected to enhance the diagnostic accuracy of heart disease, reduce the burden on medical professionals, and provide a reliable tool for early detection of heart conditions. The implementation could potentially serve as a medical aid, improving initial diagnoses and supporting healthcare practitioners.