ECG Semantic Integrator (ESI)：一种采用LLM增强心脏病学文本预训练的基础ECG模型
ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text
AI总结：
提出CQA与ESI组成的多模态对比预训练框架，利用LLM和RAG生成ECG文本并训练编码器，在心律失常检测和受试者识别上优于多种基线。
摘要：
深度学习在心电图（ECG）分析中的应用，为心脏医疗诊断带来了先进的准确性与效率。通过利用深度学习在语义理解方面的能力，尤其是在特征提取和表示学习方面的能力，本研究提出了一种新的多模态对比预训练框架，旨在提升12导联ECG信号所学表示的质量和鲁棒性。该框架包含两个关键组件：Cardio Query Assistant（CQA）和ECG Semantics Integrator（ESI）。CQA集成了检索增强生成（RAG）流程，以利用大型语言模型（LLM）和外部医学知识生成详细的ECG文本描述；所生成的文本包含丰富的人口统计学信息和波形模式信息。ESI则结合对比损失与字幕损失来预训练ECG编码器，以增强表示能力。我们通过多种下游任务验证了该方法，包括心律失常检测和基于ECG的受试者识别。实验结果表明，该方法在这些任务上相较强基线取得了显著提升；这些基线涵盖监督学习和自监督学习方法，以及以往的多模态预训练方法。
英文摘要：
The utilization of deep learning on electrocardiogram (ECG) analysis has brought the advanced accuracy and efficiency of cardiac healthcare diagnostics. By leveraging the capabilities of deep learning in semantic understanding, especially in feature extraction and representation learning, this study introduces a new multimodal contrastive pretaining framework that aims to improve the quality and robustness of learned representations of 12-lead ECG signals. Our framework comprises two key components, including Cardio Query Assistant (CQA) and ECG Semantics Integrator(ESI). CQA integrates a retrieval-augmented generation (RAG) pipeline to leverage large language models (LLMs) and external medical knowledge to generate detailed textual descriptions of ECGs. The generated text is enriched with information about demographics and waveform patterns. ESI integrates both contrastive and captioning loss to pretrain ECG encoders for enhanced representations. We validate our approach through various downstream tasks, including arrhythmia detection and ECG-based subject identification. Our experimental results demonstrate substantial improvements over strong baselines in these tasks. These baselines encompass supervised and self-supervised learning methods, as well as prior multimodal pretraining approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
