Anomaly Detection in ECG Signals Through Unsupervised Machine Learning: A Novel Approach Using Hybrid Autoencoders for Medical Data Analysis

This research introduces an innovative method for unsupervised anomaly detection in electrocardiogram (ECG) signals using autoencoders. Unlike traditional approaches relying on labeled datasets, our method utilizes a comprehensive ECG dataset. An autoencoder is employed to learn normal cardiac activity patterns and identify anomalies by assessing reconstruction errors. Evaluation on diverse anomalies, including arrhythmias, demonstrates the efficacy of the method in accurately detecting abnormal ECG patterns. The proposed approach holds promise for real-time monitoring and early detection of cardiac irregularities, contributing to enhanced patient care. This research underscores the potential of unsupervised machine learning in medical anomaly detection, laying the groundwork for automated diagnostic systems in cardiovascular health.