Scaling Pretraining Dataset Size for ECG Foundation Models and Zero-Shot Transfer Accuracy

Abstract

Abstract: Specialized foundation models are beginning to emerge in various medical subdomains, but pretraining methodologies and parametric scaling with the size of the pretraining dataset are rarely assessed systematically and in a like-for-like manner. This work focuses on foundation models for electrocardiography (ECG) data, one of the most widely captured physiological time series world-wide. We present a comprehensive assessment of pretraining methodologies, covering five different contrastive and non-contrastive self-supervised learning objectives for ECG foundation models, and investigate their s

Research Question

Does scaling the pretraining dataset size for ECG foundation models improve zero-shot transfer accuracy on non-medical sequence classification tasks compared to domain-specific fine-tuning?

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