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A multi-label ECG classification algorithm based on self-supervised pretraining and multi-modal semantic alignment
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DOI：10.1016/j.bspc.2026.109866.png)
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Abstract
En 中文
• A multi-label ECG classification algorithm is proposed in this paper. • A contrastive enhancement network with self-supervised pretraining enhances features. • A semantic-guided multi-modal fusion aligns cross-modal semantics for feature fusion. • A multi-label contrastive loss mitigates class imbalance and improves accuracy.
Keywords:
multi-label ECG classification
self-supervised pretraining
multi-modal fusion
contrastive loss
semantic alignment
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