人工智能建模光电心电图对预测心血管疾病有用
Zhengyao Ding1, Yujian Hu2, Ziyu Li1
1College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
NPJ digital medicine
|December 14, 2025
概括
心脏PPG通过结合光心电图 (PPG) 与心电图 (ECG) 数据来增强心血管疾病 (CVD) 的检测. 这种新的方法改善了非侵入性心血管疾病查,并使心电图合成能够更好地解释.
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
- 心脏病学 心脏病学
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因,需要早期检测.
- 摄影电磁镜 (PPG) 是一种非侵入性监测工具,但其准确性受到噪音和缺乏直接电生理标记物的限制.
- 现有的方法在各种环境中难以可靠地检测心血管疾病.
研究的目的:
- 开发 CardioPPG,一个交叉模式的学习框架,以增强 PPG 信号与来自 ECG 的信息,以改进 CVD 预测.
- 为了使高质量的心电图 (ECG) 信号能够使用PPG数据进行合成,以提高可解释性.
- 创建适用于家庭和资源有限的环境的可扩展,实时,非侵入性CVD监控解决方案.
主要方法:
- 实施了跨模式的对比学习方法,在共享的潜在空间中调整PPG和ECG特征.
- 利用自回归生成模型从PPG数据中合成现实的心电图信号.
- 对多个心血管疾病数据集进行了广泛的评估,包括膜疾病,心房动,心肌病和心跳动等.
主要成果:
- 心脏PPG与仅PPG的基线相比显著改善,相对AUC增幅为41.3%的额头膜疾病,12.2%的大动脉膜疾病,8.8%的心肌病,和30.3%的上心脉上心力衰竭.
- 在外部心房动数据集上达到99.5%和98.6%的高AUC,证实了概括性.
- 生成的合成心电图信号与真实心电图的分布密切匹配,提高了模型的可解释性.
结论:
- CardioPPG提供了一种强大的,非侵入性的方法,可以通过ECG洞察来增强PPG信号,显著提高心血管疾病查准确度.
- 该框架合成ECG的能力提高了可解释性和临床实用性.
- CardioPPG为可访问的实时心血管疾病监测提供了一个有希望的解决方案,在各种环境中促进早期检测和干预.
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