Electrocardiogram Classification with Transformers Using Koopman and Wavelet Features
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). We use Extended Dynamic Mode Decomposition (EDMD) to approximate the Koopman operator. Our results show that wavelet features excel in binary classification, while Koopman features, when paired ...
| Authors | Sucheta Ghosh, Zahra Monfared |
|---|---|
| Primary category | eess.SP |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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