从脉冲到睡眠阶段:使用心率变化优化睡眠分类
Pavlos I Topalidis1, Sebastian Baron2,3, Dominik P J Heib1,4
1Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, Centre for Cognitive Neuroscience Salzburg (CCNS), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
可穿戴的睡眠追踪器可能不准确,导致"orthosomnia. " 这项研究使用新的损失函数模型优化了睡眠分类,提高了消费者可穿戴设备的准确性,即使使用药物也显示出可靠的结果.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠科学 睡眠科学
- 数字健康数字健康
背景情况:
- 消费者用于睡眠跟踪的可穿戴设备越来越受欢迎,但它们的准确性经常受到批评,这可能会导致睡眠跟踪问题.
- 这种疾病是orthosomnia.
- 和负面的健康结果.
研究的目的:
- 优化以前开发的睡眠分类程序,用于使用消费者可穿戴设备进行门诊睡眠检测.
- 通过引入先进的信号质量控制和一种新的损失函数模型,尽量减少自我报告的睡眠不良者的错误睡眠分类.
主要方法:
- 采用了一种新的节拍间隔 (IBI) 质量控制方法,采用随机森林算法.
- 实施睡眠分类的损失函数模型,将其与时代对时代准确性模型进行对比.
- 使用多睡眠学 (PSG),单导电心电图,Polar® H10 (ECG) 和Polar® Verity Sense (PPG) 可穿戴设备的信号进行分类性能比较.
主要成果:
- 优化的损失函数模型实现了高整体精度 (86.3%的心电图,84.4%的H10,84.2%的VS),在深度睡眠和清醒分类方面取得了显著的改进.
- 该模型显示了初级睡眠参数与PSG的中度至高相关性和一致性.
- 对于大多数睡眠参数观察到的可靠性很好,在服用心脏影响或精神活性药物的人群中,保持了准确的4级睡眠阶段.
结论:
- 优化的睡眠分类模型提高了可穿戴睡眠传感器的准确性和可靠性,解决了先前的批评.
- 这种方法对临床应用有希望,特别是对于老年人或患有常见疾病的人来说,通过提供可靠的睡眠数据.
- 进一步验证这些算法可以增加对可穿戴睡眠技术的信心,并促进其融入临床实践.
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