Contrastive Learning for Multi-Label ECG Classification with Jaccard Score–Based Sigmoid Loss

Recent advances in large language models (LLMs) have enabled the development of multimodal medical AI. While models such as MedGemini achieve high ac- curacy on VQA tasks like USMLE-MM, their performance on ECG-based tasks remains limited, and some models, such as MedGemma, do not support ECG data at all. Interpreting ECGs is inherently challenging, and diagnostic accuracy can vary depending on the interpreter’s experience. Although echocardiography pro- vides rich diagnostic information, it requires specialized equipment and personnel, limiting its availability. In this study, we focus on constructing a robust ECG encoder for multimodal pretraining using real-world hospital data. We employ SigLIP, a CLIP-based model with a sigmoid-based loss function enabling multi-label prediction, and introduce a modified loss function tailored to the multi-label nature of ECG data. Experiments demonstrate that incorporating medical knowledge in the language model and applying the modified loss significantly improve multi-label ECG classification. To further enhance performance, we increase the embedding dimensionality and apply random cropping to mitigate data drift. Finally, per-label analysis reveals which ECG findings are easier or harder to predict. Our study provides a foundational framework for developing medical models that utilize ECG data.