使用多式体网络和昼夜节律的睡眠阶段预测
Sahar Waqar1, Muhammad Usman Ghani Khan2
1Department of Computer Engineering, University of Engineering and Technology, Lahore, Lahore, Punjab, Pakistan.
PeerJ. Computer science
|April 30, 2024
概括
这项研究使用可穿戴设备来预测睡眠阶段和模式. 基于记忆的模型,特别是长期短期记忆 (LSTM),在捕捉睡眠动态方面表现出优异的性能,与无记忆方法相比.
科学领域:
- 生物医学工程 生物医学工程
- 计算神经科学是一种神经科学.
- 睡眠科学 睡眠科学
背景情况:
- 良好的睡眠对于生理恢复和废物清除至关重要.
- 睡眠障碍与各种健康问题有关,包括抑郁症和代谢问题.
- 了解睡眠周期和阶段对于诊断和管理睡眠障碍至关重要.
研究的目的:
- 利用可穿戴设备的数据开发和评估用于预测睡眠阶段和模式的模型.
- 为了比较无记忆 (ML) 和基于记忆 (LSTM) 模型在睡眠阶段预测中的有效性.
- 在各种数据集上验证拟议的方法,包括真实世界和基准数据.
主要方法:
- 从使用可穿戴设备的受试者收集了多参数数据.
- 应用了各种无记忆分类器 (随机森林,LR,MLP,kNN,SVM) 和基于记忆的模型 (LSTM).
- 使用精度 (ACC) 和Cohen Kappa在五个定制和两个公共数据集上评估模型性能.
主要成果:
- 随机森林在没有记忆的模型中实现了高精度 (0.96) 和Kappa (0.96).
- LSTM在数据集中表现出强的表现,最高准确率为0.88和Kappa为0.82.
- 该方法超越了原始工作,基于记忆的模型显示了更好的睡眠模式捕获.
结论:
- 可穿戴设备数据与先进的建模相结合,可以准确预测睡眠阶段和模式.
- 基于记忆的模型,特别是LSTM,在捕捉睡眠的时间动态方面比没有记忆的模型更有效.
- 这种方法对非侵入性睡眠监测和个性化睡眠健康管理具有前景.
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