A novel multimodal self-supervised framework for ECG arrhythmia classification

Jianqiang Hu

doi:10.1016/j.compbiomed.2025.111137

A novel multimodal self-supervised framework for ECG arrhythmia classification

2025-10-06

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DOI：10.1016/j.compbiomed.2025.111137

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• SimECG: a novel siamese network for self-supervised ECG representation learning. • New loss functions for 1D and 2D ECG data using contrastive and alignment loss. • Superior performance validated against other contrastive learning frameworks.

A novel multimodal self-supervised framework for ECG arrhythmia classification

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