睡眠阶段的时间序列视觉表示,睡眠阶段的分类
Rebeca Padovani Ederli1, Didier A Vega-Oliveros2, Aurea Soriano-Vargas3
1Institute of Computing, University of Campinas (Unicamp), Campinas, SP, Brazil.
PloS one
|May 21, 2025
概括
智能手表数据转化为视觉表示,如格拉米安角场,与传统方法相比,显著提高了睡眠阶段分类的准确性. 这为睡眠监测和健康洞察提供了更容易获得和更有效的方法.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠科学 睡眠科学
- 人工智能的人工智能
背景情况:
- 多睡眠学 (PSG) 是睡眠阶段分类的黄金标准,但对于家庭使用来说是昂贵和不切实际的.
- 智能手表为连续睡眠监测提供了一个方便,非侵入性和经济高效的替代方案.
- 传统的智能手表睡眠分析人工智能方法通常使用原始或提取的时间序列数据.
研究的目的:
- 为了研究将时间序列智能手表数据转换为视觉表示以改进睡眠阶段分类的有效性.
- 使用智能手表数据,比较视觉表示方法与睡眠阶段分类传统方法的性能.
主要方法:
- 智能手表加速度计和心率传感器的时间序列数据被转换成视觉格式 (格拉米安角场,复发图,马尔科夫过渡场,光谱图).
- 对这些视觉表征应用了二维卷积神经网络.
- 使用图像补丁和组合方法来提高分类性能.
主要成果:
- 格拉米安的角度场,结合补丁和合奏技术,实现了最高的性能.
- 拟议的方法显示出卓越的准确性,超过82%的两个阶段的睡眠分类和62%的三个阶段的睡眠分类.
- 这种方法比传统方法显著改善,高达8-9个百分点.
结论:
- 智能手表时间序列数据的视觉表示对于睡眠阶段分类非常有效.
- 这种方法超越了传统的方法,为睡眠监测提供了具有竞争力和可靠的替代方案.
- 这些发现支持使用视觉表示来加强健康监测和及时干预.
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