开放式机器学习模型的性能,在不知休息时间的情况下,在24小时的间隔内从动图分类睡眠/觉醒
Daniel M Roberts1, Margeaux M Schade2, Lindsay Master2
1Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, USA; Proactive Life, Inc, New York, New York, USA.
Sleep health
|August 12, 2023
概括
这项研究开发了一种新的睡眠/清醒分类器,使用24小时间隔的动图数据. 新的时间卷积网络模型的性能与现有方法相比或更好,即使不知道入床时间.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠科学 睡眠科学
- 机器学习 机器学习
背景情况:
- 动图算法通常需要已知的床上间隔来进行睡眠/清醒分类.
- 自由生活条件往往缺乏精确的床上定时信息,限制了传统算法性能.
研究的目的:
- 开发和评估一个睡眠/清醒分类器,使用24小时长的动图数据进行分类.
- 为了实现连续的睡眠/清醒分类,而无需事先了解床上间隔.
主要方法:
- 一个时卷积网络 (TCN),一个深层神经网络,使用监督学习方法进行训练.
- 来自ActiWatch Spectrum设备的Actigraphy计数用于培训和评估.
- 性能与奥克利动画算法和多睡眠学衍生的睡眠阶段对比进行了基准测试.
主要成果:
- 在所有测试的指标上,TCN分类器在24小时间隔内与奥克利算法相比表现同等或更好.
- TCN分类器在几个指标上保持了良好的表现,即使分析仅限于床内间隔.
- 应用到动脉样硬化多民族研究 (MESA) 数据集显示性能下降,特别是在床上间隔内,突出了概括性挑战.
结论:
- 一个基于24小时动图的分类器可以实现连续的睡眠/清醒分类,而不需要在床上定时.
- 开发的TCN模型显示了改善自由生活环境中的动画分析的前景.
- 未来的研究应该优先提高模型的概括能力,以便更广泛地应用.
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