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基于Mamba的深度学习方法,用于在无线多式联机可穿戴系统上进行睡眠分期,而无需脑电图
Andrew H Zhang1,2,3, Alex He-Mo1,2, Richard Fei Yin1,2
1University of Toronto, Toronto, ON, Canada.
基于Mamba的深度学习模型使用ANNE One可穿戴系统准确地分阶段睡眠,为临床环境中睡眠分析提供非EEG替代方案.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
背景情况:
- 可穿戴技术提供了一种非侵入性的方法来收集与睡眠阶段相关的生理数据.
- 传统的多睡眠学 (PSG) 需要专门的设备和临床环境,限制了广泛的睡眠分析.
- 为可穿戴传感器开发精确的睡眠分期算法对于远程和可访问的睡眠监控至关重要.
研究的目的:
- 通过使用ANNE One可穿戴系统的数据,评估基于Mamba的深度学习模型用于睡眠分阶段.
- 评估模型在不同睡眠阶段分类 (3,4,5类) 的性能.
- 确定使用非EEG可穿戴数据用于临床睡眠分析的可行性.
主要方法:
- 从357名成年人同时收集了可穿戴传感器数据 (心电图,加速度计,温度,PPG).
- 在可穿戴传感器数据上训练了一种基于Mamba的循环神经网络架构,使用手动得分的PSG作为基准真相.
- 组装技术应用于模型变体以提高性能.
主要成果:
- 组装的Mamba模型实现了3类平衡精度的84.02%和Cohen的72.89%的kappa.
- 对于4级睡眠阶段,该模型达到75.30%的平衡精度和74.10%的F1得分.
- 性能随着睡眠阶段的更细致的分类而变化,这表明模型的进一步完善的潜力.
结论:
- 基于Mamba的深度学习模型有效地从ANNE One可穿戴系统中推断出主要睡眠阶段.
- 这种方法在临床睡眠实验室环境中为成年人睡眠分阶段提供了一个可行的非EEG替代方案.
- 这些发现支持将这种可穿戴技术和人工智能模型应用于更广泛的睡眠健康监测.
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