Holtercare-Bench：用于评估长期动态心电图分析的多模态基准

中文摘要 AI 辅助

尽管多模态大语言模型（MLLMs）在医疗应用中表现出色，但大多数模型更倾向于静态图像或短期信号。在关键的动态心电图（ECG）领域，由于缺乏高质量数据集和基准，模型在复杂的时间推理和诊断报告生成方面存在困难。为解决这一问题，我们推出了（i）Holtercare-23K，这是一个大规模多模态动态ECG数据集，包含来自788条临床动态心电图（Holter）记录的22980个问答对，具有新颖的信号-视频-文本三模态对齐特性。基于该数据集，我们提出了（ii）Holtercare-Bench，这是一个多模态基准，用于评估模型的时间定位、临床诊断和全局总结能力。对领先MLLMs的零样本评估显示，其在处理超长病理序列时存在显著性能差距，但对代表性模型进行微调后可获得大幅提升。本研究阐明了当前MLLMs在电生理学领域的局限性，并为长期医疗MLLMs提供了基础基准，项目地址为该URL。

英文摘要

While multimodal large language models (MLLMs) excel in medical applications, most of them favor static images or short-term signals. In the critical field of dynamic electrocardiograms (ECG), models struggle with complex temporal reasoning and diagnostic report generation due to a lack of high-quality datasets and benchmarks. To address this, we introduce (i) Holtercare-23K, a large-scale multimodal dynamic ECG dataset comprising 22,980 QA pairs derived from 788 clinical Holter records and featuring a novel signal-video-text tri-modal alignment. Based on this dataset, we present (ii) Holtercare-Bench, a multimodal benchmark that evaluates models on temporal localization, clinical diagnosis, and global summarization. Zero-shot evaluations of leading MLLMs reveal a significant performance gap in processing ultra-long pathological sequences. However, fine-tuning representative models yields substantial improvements. This work illuminates the limitations of current MLLMs in electrophysiology and provides a foundational benchmark for long-term medical MLLMs. Our project is available at https://github.com/ZJU4HealthCare/Holtercare-Bench.

发表机构

• Zhejiang University（浙江大学）
• Beijing Institute of Technology（北京理工大学）
• University of Electronic Science and Technology of China（电子科技大学）

机构由 AI 辅助整理，请以论文原文为准。