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

TL;DR

This paper introduces a contrastive learning approach with a Jaccard score-based sigmoid loss for multi-label ECG classification, improving diagnostic accuracy and interpretability in medical AI applications.

Contribution

It presents a novel ECG encoder using SigLIP with a modified loss function tailored for multi-label data, enhancing ECG classification performance.

Findings

Incorporating medical knowledge improves classification accuracy.

Modified loss function outperforms standard approaches.

Increasing embedding size and data augmentation enhances robustness.

Abstract

Recent advances in large language models (LLMs) have enabled the development of multimodal medical AI. While models such as MedGemini achieve high accuracy 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 provides 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.