AI RESEARCH
Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias
arXiv CS.LG
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ArXi:2506.16494v3 Announce Type: replace Electrocardiograms (ECGs) provide non-invasive measurements of heart activity and are established tools for detecting cardiac arrhythmias. Although supervised machine learning has emerged as a promising approach for automated heartbeat classification, substantial variations in ECG signals across individuals and leads, combined with inconsistent labeling standards and dataset biases, make it difficult to develop generalizable models.