AI RESEARCH
Electrocardiogram Classification with Transformers Using Koopman and Wavelet Features
arXiv CS.LG
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ArXi:2603.08339v1 Announce Type: cross Electrocardiogram (ECG) analysis is vital for detecting cardiac abnormalities, yet robust automated classification is challenging due to the complexity and variability of physiological signals. In this work, we investigate transformer-based ECG classification using features derived from the Koopman operator and wavelet transforms. Two tasks are studied: (1) binary classification (Normal vs. Non-normal), and (2) four-class classification (Normal, Atrial Fibrillation, Ventricular Arrhythmia, Block.