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CMA-ECG:跨模式的注意力提高了ECG质量评估和拒绝

Hong Duc Nguyen¹, Duc Tri Phan²

¹School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.

Physiological measurement

|October 21, 2025

概括

本研究介绍了CMA-ECG,这是一种使用大型语言模型改进心电图 (ECG) 分析的新框架. 它增强了信号消音和质量评估,以实现更可靠的心脏监测.

科学领域:

生物医学工程生物医学工程
医疗保健中的人工智能
心血管信号处理系统

背景情况:

电心电图 (ECG) 分析对于诊断心脏病和监测生理学至关重要.
电脑心电图数据经常受到干扰,质量不一致和推断的影响,影响可靠性.
大型语言模型 (LLM) 显示出增强心电图测量的潜力,但尚未得到充分探索.

研究的目的:

开发一个新的多式联通框架,用于强大的ECG报警和信号质量评估.
将心电图信号与文本说明相结合,以改善生理分析.
为了应对信号干扰和ECG数据质量的挑战.

主要方法:

提出了跨模式注意力 (CMA) -ECG框架,一种多式模式的方法.
使用混合交叉注意力机制来调整ECG和文本功能.
集成了一个预先训练的LLM (7B参数) 用于上下文增强和特定任务的头部.

主要成果:

在稳定性,质量评估和反方面,CMA-ECG取得了最先进的 (SOTA) 绩效.
在质量评估的ROC曲线下显示的面积高达8.8%.
与SOTA基线相比,实现了20%较低的平均平方误差.

结论:

CMA-ECG方法为生理监测和分析提供了一个强大的解决方案.
整合信号和上下文数据可以提高心电图分析的可靠性.
这一框架确保了在各种应用中可靠的ECG处理.

关键词:

质量评估的ECG质量评估基于心电图的生理分析.跨模式的注意力.电心电图 (ECG) 是一种心电图.大型语言模型 (LLM)

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