A novel multimodal self-supervised framework for ECG arrhythmia classification

The electrocardiogram (ECG) has emerged as a primary tool in clinical practice for identifying cardiovascular diseases, owing to its low cost, simplicity, and non-invasiveness. Given the high cost associated with acquiring a substantial amount of ECG signals that require annotation by medical profes...

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Bibliographic Details
Published in:Computers in biology and medicine 2025-11, Vol.198 (Pt A), p.111137-111137, Article 111137
Main Authors: Hu, Jianqiang, Li, Cheng, Cao, Jinde, Kou, Bo
Format: Article
Language:English
Subjects:
Algorithms
Arrhythmias, Cardiac - classification
Arrhythmias, Cardiac - diagnosis
Arrhythmias, Cardiac - physiopathology
Contrastive learning
Databases, Factual
ECG classification
Electrocardiography - methods
Humans
Internal Medicine
Other
Self-supervised learning
Signal Processing, Computer-Assisted
Supervised Machine Learning
Time-frequency
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