多类心律失常的分类使用多式智能手表光电磁缩学信号收集在现实生活中的设置
IEEE transactions on bio-medical engineering
|September 23, 2025
概括
这项研究开发了智能手表的深度学习模型,使用PPG数据准确检测心房动 (AF) 和早/心室收缩 (PAC/PVC). 该模型提高了检测精度和效率,提高了可穿戴AF监测的临床接受度.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 带有光聚血学 (PPG) 传感器的智能手表可为早期心房 (AF) 检测提供持续监测.
- 以前用于AF检测的深度学习模型受到受控环境,短数据持续时间和难以区分AF与过早心房/心室收缩 (PAC/PVC) 的限制.
- 对于PAC/PVC检测的有限数据集阻碍了当前最先进的方法的性能,仅达到75%的灵敏度.
研究的目的:
- 使用智能手表PPG数据解决AF和PAC/PVC检测的局限性.
- 开发一个计算效率高的深度学习模型,用于准确检测心律失常.
- 提高AF和PAC/PVC检测模型在不同数据集和设备上的通用性.
主要方法:
- 利用了NIH资助的Pulsewatch临床试验的数据,从106名受试者收集了两周的智能手表PPG数据.
- 开发了一种1D双向Gated Recurrent Unit深度学习模型,包含多模输入 (PPG,加速度计,心率).
- 分类数据分为正常鼻节律,AF和PAC/PVC类别.
主要成果:
- 在PAC/PVC检测方面获得了83%的灵敏度,在AF检测方面获得了97.31%的准确度,显著优于之前的方法.
- 该模型展示了14倍更高的计算效率和2.7倍更快的处理速度.
- 在外部数据集上验证了概括性,实现了96.22%和94.17%的宏观平均AUROC值.
结论:
- 一个轻量级的多式联机深度学习模型可以准确地区分PAC/PVC与AF,减少错误的阳性.
- 在检测AF和PAC/PVC时,提高精度可以增加临床和公众对基于智能手表的AF监测的信任.
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