在接受选择性电心转换的患者中,基于视频的远程 supraventricular tachycardia检测的准确性:一个潜在的队列
Iris Cramer1,2, Rik van Esch3,4, Cindy Verstappen3,5
1Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AZ, Eindhoven, the Netherlands. I.c.cramer@tue.nl.
Journal of clinical monitoring and computing
|January 29, 2025
概括
使用视频的远程光电脉扫描 (rPPG) 准确地检测正常鼻节律和心律失常期间的脉率. 这种机器学习模型显示出对不引人注目的患者监测的希望,尽管在心律失常期间低估心率需要进一步优化.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 人工智能的人工智能
背景情况:
- 远程光电脉扫描 (rPPG) 通过视频提供不引人注目的脉率监测.
- 早期发现外科手术期间心律失常对于患者的治疗结果至关重要.
- 基于rPPG的机器学习对于在心律失常期间的脉冲率监测的准确性仍然在很大程度上是未知的.
研究的目的:
- 评估机器学习模型的准确性,使用rPPG监测有或没有心律失常的患者的脉率.
- 用可见光摄像头评估模型在现实环境中的性能.
主要方法:
- 进行了一项前性观察性诊断研究.
- 使用rPPG和心电图 (ECG) 来测量脉率,作为对电心转换患者的参考.
- 一个受监督的机器学习模型 (支持矢量机器) 使用60秒rPPG信号间隔进行训练和验证.
主要成果:
- 机器学习模型在识别鼻节律和心律失常方面表现出强大的区别 (AUC 0.95).
- 脉率测量显示,鼻节律的偏差为1.21,心律不整的偏差为-7.45.
- 该模型在现实环境中准确地识别了正常鼻节律和心律失常.
结论:
- 使用rPPG的机器学习模型可以在正常鼻节律和心律失常期间准确识别脉冲率.
- 这项技术显示出在普通病房患者中不引人注目的外科手术期间心律失常检测的潜力.
- 需要进一步优化,以解决在心律失常事件期间观察到的心率低估的问题.
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