GATE: Graph and Text Exchange for Zero-Shot ECG Classification with LLM Prompts.

Abstract

Electrocardiography (ECG) is a fundamental tool for diagnosing cardiovascular diseases, yet the scarcity of large-scale annotated data limits the applicability of supervised learning approaches. While self-supervised learning (SSL) has shown promise for ECG representation learning, existing methods often suffer from semantic distortion, insufficient spatial modeling, and a lack of integration with medical knowledge. To address these challenges, we propose GATE (Graph-And-Text Exchange), a novel multimodal SSL framework that enhances the quality of the representation of ECG through cross-modal exchange between graph-structured data and clinical ECG reports. GATE employs a spatiotemporal graph encoder to capture fine-grained intra- and inter-lead dependencies, and introduces a lexical knowledge-embedded codebook to enhance the semantic representation of clinical reports, facilitating effective graph-text alignment. During inference, GATE integrates a large language model with a domain-specific knowledge base to generate semantically enriched disease descriptions, enabling robust zero-shot classification. Extensive experiments on three real-world ECG datasets demonstrate that GATE outperforms state-of-the-art self-supervised and multimodal baselines under both low-resource and zero-shot settings. Notably, GATE achieves competitive performance even when trained on only 1% of labeled data, highlighting its strong generalization and clinical potential.