通过适应便携和可穿戴设备的单导电心电图检测左心室缩功能障碍
Akshay Khunte1, Veer Sangha1, Evangelos K Oikonomou2
1Department of Computer Science, Yale University, New Haven, CT, USA.
NPJ digital medicine
|July 11, 2023
概括
一个新的AI策略有效地检测左心室缩功能障碍 (LVSD) 来自可穿戴设备中常见的杂,单导电心电图. 这种适应噪声的模型显著提高了临床环境之外的心血管查的准确性.
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:
- 人工智能 (AI) 在通过心电图 (ECG) 来检测左心室缩功能障碍 (LVSD) 方面表现有前途.
- 可穿戴设备提供了基于人工智能的广泛心血管查的潜力,但往往会产生噪音的心电图数据.
- 现有的AI模型在与可穿戴传感器典型的信号质量退化作斗争.
研究的目的:
- 开发和验证一种新的人工智能策略,用于检测来自杂的单ECG的LVSD.
- 从可穿戴和便携设备获得的ECG上适应AI模型以提供强大的性能.
- 为了提高人工智能驱动的心血管疾病查的可靠性,使用现实世界的噪音数据.
主要方法:
- 开发两个人工智能模型:一个标准模型和一个适应噪声的模型,使用385,601个ECG.
- 在训练过程中增加了ECG数据,用于模拟现实世界的噪声,在四个频率范围内对噪声适应模型进行训练.
- 两种模型在标准ECG和通过各种噪声源人工降解的ECG上进行比较评估,包括在不同的信号噪声比率 (SNR) 上的便携式设备噪声.
主要成果:
- 标准模型和适应噪声的模型在清洁的ECG上实现了相似的性能 (AUROC 0.90).
- 适应噪声的模型在噪声高的ECG上显著优于标准模型.
- 在增加便携式设备噪声 (SNR 0.5) 的心电图上,适应噪声的模型实现了0.87的AUROC,而标准模型的AUROC为0.72.
结论:
- 一种新的噪音适应策略提高了人工智能模型的性能,用于从杂的可穿戴式ECG中检测LVSD.
- 这种方法使得使用大型临床心电图数据集开发出可穿戴,适应心血管查的强大AI工具.
- 这些发现支持通过便携式设备进行广泛的基于人工智能的心血管健康监测的可行性.
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