Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis
Through Self-Supervised Learning
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis
Through Self-Supervised Learning
The electrocardiogram (ECG) is an essential tool for diagnosing heart disease, with computer-aided systems improving diagnostic accuracy and reducing healthcare costs. Despite advancements, existing systems often miss rare cardiac anomalies that could be precursors to serious, life-threatening issues or alterations in the cardiac macro/microstructure. We address this gap by focusing …