Search Results for author:
Found 2 papers, 1 papers with code
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning
We introduce a novel self-supervised learning framework for ECG AD, utilizing a vast dataset of normal ECGs to autonomously detect and localize cardiac anomalies.
Self-Supervised Anomaly Detection
Self-Supervised Learning
+2
Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection
To address this challenge, this paper introduces a novel multi-scale cross-restoration framework for ECG anomaly detection and localization that considers both local and global ECG characteristics.
