ECGWavePuzzle as Morphology-Aware Auxiliary Supervision for Multitask Arrhythmia Classification - Silva, Guilherme; Negrão, Arthur; Silva, Pedro; Luz, Eduardo (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2026.21399, 2026)
Arrhythmia detection with deep learning often relies on heavy preprocessing and synthetic oversampling, obscuring whether models truly learn cardiac patterns. We propose a multitask self-supervised framework that jointly optimizes arrhythmia classification, RR-interval regression, and the ECGWavePuzzle pretext task. This design encourages the model to capture both temporal rhythm and intra-beat morphology directly from raw ECG signals. Evaluated on MIT-BIH, PTB-XL, and IEGM, the approach improves stability and predictive performance over single-task baselines without synthetic data generation. Our results suggest that physiologically grounded auxiliary objectives provide a more principled path to robust ECG representation learning.