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This work discusses the use of contrastive learning and deep learning fordiagnosing cardiovascular diseases from electrocardiography (ECG) signals.While the ECG signals usually contain 12 leads (channels), many healthcarefacilities and devices lack access to all these 12 leads. This raises theproblem of how to use only fewer ECG leads to produce meaningful diagnoses withhigh performance. We introduce a simple experiment to test whether contrastivelearning can be applied to this task. More specifically, we added thesimilarity between the embedding vectors when the 12 leads signal and the fewerleads ECG signal to the loss function to bring these representations closertogether. Despite its simplicity, this has been shown to have improved theperformance of diagnosing with all lead combinations, proving the potential ofcontrastive learning on this task.

Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata