一项试点研究:通过可穿戴设备和深度学习自动监测异常REM睡眠行为障碍的身体功能障碍
概括
这项研究表明,可穿戴传感器和深度学习可以通过分析体检期间患者的运动来有效监测异常REM睡眠行为障碍 (iRBD),从而为传统方法提供了具有成本效益的替代方案.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 异常性 快速眼动 (REM) 睡眠行为障碍 (iRBD) 是帕金森病 (PD) 的前兆标记.
- 目前的诊断和监测方法,如视频多睡眠镜像 (vPSG) 是昂贵的,并有局限性,如第一夜效应.
- 需要为iRBD提供可访问的,持续的监控解决方案.
研究的目的:
- 为iRBD探索一种经济且持续的家庭监控方法.
- 使用可穿戴传感器自动识别和分析iRBD患者的体检表现.
- 评估深度学习算法在区分iRBD患者与帕金森病患者和基于运动性能的健康对照中的潜力.
主要方法:
- 招募了11名iRBD患者,从MDS-UPDRS第三部分进行8次体检,使用放置在手腕,脚和腹部的加速度计.
- 开发了一种用于活动识别的新通道注意力集成时间卷积网络 (STCN),获得了高F1分数.
- 利用PD-BIOSTAMPRC21数据集,包括17名PD患者和14名健康对照,进行比较分析.
主要成果:
- 对于活动识别,STCN模型获得了0.939 ± 0.106的F1得分.
- 在iRBD,PD和健康对照组之间,在特定的运动任务中观察到显著差异,例如手指敲击 (p = 0.046) 和脚敲击 (p = 0.049).
- 从加速度计信号中提取了七个不同的特征,以分析性能变化.
结论:
- 可穿戴传感器技术与深度学习相结合,为监测iRBD提供了一种可行且具有成本效益的方法.
- 对UPDRS III测试性能的自动化分析显示了持续iRBD患者跟踪的潜力.
- 这种方法可以帮助早期检测和管理与iRBD相关的神经退行性疾病.
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