研究跨数据集的心脏信号域概括和特征解释性
Runwen Li1, Yierpani Aierken2, Yu Xu3
1Department of Vascular Diseases, Panzhihua Central Hospital, Panzhihua, 617000, China. RunwenLi@outlook.com.
Scientific reports
|December 21, 2025
概括
本研究介绍了一个基于变压器的框架,以改进跨域心电图 (ECG) 建模. 该方法提高了对心律失常检测的概括性和解释性,而不需要目标域数据.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 电心电图 (ECG) 信号的交叉数据集建模受到领域转移和缺乏临床解释性的挑战.
- 现有的方法很难在不同的心电图数据集中进行概括,并提供透明的临床见解.
研究的目的:
- 为跨域ECG建模提出基于变压器的框架,以提高概括性和可解释性.
- 解决领域转移问题,提高自动化心电图分析的临床适用性.
主要方法:
- 开发了一个基于变压器的框架,包括一个域不变特征增强模块 (DFEM) 和一个基于解释性的注意力约束机制 (IACM).
- 在严格的实验中使用了四个权威的心律失常数据库 (MIT-BIH 超心室,MIT-BIH 心律失常,INCART,SCD-Holter).
- 进行了废除研究,特征重要性分析和t-SNE可视化以验证模型组件和可解释性.
主要成果:
- 拟议的方法在所有目标领域实现了卓越的性能,超过了基线变压器模型.
- 在MIT-BIH心律失常数据集上实现了8.4%的更高准确度 (0.768),在INCART数据集上提高了4.7%的F1得分 (0.898).
- 废除研究证实了DFEM和IACM的有效性,而分析显示该模型重点关注临床相关的ECG特征.
结论:
- 拟议的框架有效地减轻了ECG建模中的域移动,而不需要目标域样本.
- 该方法同时提高了模型的可解释性和临床应用的分辨性性能.
- 这种方法提供了一个有希望的解决方案,用于对心律失常的强大和透明的自动化分析.
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