Revisiting masked auto-encoders for ECG-language representation learning
Year: 2024
Type: conference-paper
Abstract: We propose C-MELT, a novel framework for multimodal self-supervised learning of Electrocardiogram (ECG) and text encoders. C-MELT pre-trains a contrastive-enhanced masked auto-encoder architecture using ECG-text paired data. It exploits the generative strengths with improved discriminative capabilities to enable robust cross-modal alignment. This is accomplished through a carefully designed model, loss functions, and a novel negative sampling strategy. Our preliminary experiments demonstrate significant performance improvements with up to 12% in downstream cardiac arrhythmia classification and patient identification tasks. Our findings demonstrate C-MELT's capacity to extract rich, clinically relevant features from ECG-text pairs, paving the way for more accurate and efficient cardiac diagnoses in real-world healthcare settings.
Authors: PHAM, Hung Manh, SAEED, Aaqib, MA, Dong
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7033742162
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