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Continuity Contrastive Representations of ECG for Heart Block Detection from Only Lead-I
Proceedings of the 4th Machine Learning for Health Symposium, PMLR 259:130-142, 2025.
Abstract
Early detection of heart block can prevent life-threatening outcomes in patients with cardiac conduction disorders. While 12-lead ECG interpretation is the clinical standard apparatus, this work investigates detecting heart block from the lead-I ECG signals, the lead available on commercial smartwatches. We evaluate two state-of-the-art architectures: residual neural network and transformer encoder, both trained in a self-supervised contrastive learning manner with a novel signal-continuity-based ECG view definition on a dataset of 3.6 million ECGs from Massachusetts General Hospital. These models learn efficient ECG representations, which are used for heart block detection via linear probing on the PTB-XL dataset, a public ECG resource. To provide performance benchmarks, we compare our self-supervised models to supervised adaptations of both models trained on 10.6 thousand single-lead PTB-XL ECGs. Our analysis evaluates the performance of each model using the area under the receiver-operating curve (AUC), sensitivity, and specificity. We observe improved performance from the self-supervised pretraining. Additionally, we demonstrate the robust generalizability of these models in scarce-data scenarios, maintaining consistent performance with a reduced number of labeled training examples. This study highlights the potential of self-supervised learning in lead-I ECG diagnostics, offering promising implications for clinical applications where labeled data is scarce.