在果手表和卡迪亚移动心电图中检测心房动的一个众包人工智能框架
Ali Bahrami Rad1, Miguel Kirsch2, Qiao Li1
1Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
使用多个来源的新投票算法显著改善了移动心电图设备上的心房动 (AFib) 检测. 这种众包方法提高了不同平台的准确性和稳定性,克服了当前人工智能心脏监测的关键局限性.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 数字健康数字健康
背景情况:
- 移动心电图设备提供了有前途的心房动 (AFib) 检测.
- 目前的AFib算法缺乏跨不同数据集和平台的概括性.
- 这限制了人工智能驱动的心脏监测的现实应用.
研究的目的:
- 为移动心电图装置开发一种强大且可通用的AFib检测方法.
- 为了利用众包算法来提高AFib检测准确度.
- 解决人工智能在可穿戴心脏监测方面面临的泛化挑战.
主要方法:
- 开发了一个随机森林投票算法,整合了六个开源AFib检测算法.
- 投票算法是在AliveCor数据集上训练的.
- 在分离的AliveCor和Apple Watch数据集上评估了性能.
主要成果:
- 投票算法在敏感性,特异性,PPV,NPV和F1得分方面表现优于个别基础算法.
- 它在各种数据集中表现出卓越的稳定性和有效性,变化最小.
- 这种方法超越了果的算法,并显示了与AliveCor的Kardia算法相比具有竞争力的性能.
结论:
- 众包,多算法策略增强了AFib检测能力.
- 开发的方法表现出强大的跨平台性能,解决了泛化挑战.
- 协作算法显示了改善可穿戴心脏监测设备的巨大潜力.
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