一个新的持续睡眠状态人工神经网络模型,基于多功能融合的多睡眠数据
Jian Cui1, Yunliang Sun2, Haifeng Jing3
1Department of Big Data and Fundamental Sciences, Shandong Institute of Petroleum and Chemical Technology, Dongying, Shandong, 257061, People's Republic of China.
Nature and science of sleep
|June 19, 2024
概括
这项研究引入了一个新的人工神经网络模型,从EEG数据中创建一个连续的睡眠深度值 (SDV),改进了超越传统阶段的睡眠分析. 该模型更好地捕捉了睡眠细微差别和唤醒模式,特别是在阻塞性睡眠呼吸暂停-呼吸暂停综合征 (OSAHS) 患者中.
科学领域:
- 睡眠科学 睡眠科学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 传统的睡眠分期依赖于30秒的时代,这可能会错过微妙的睡眠动态.
- 了解睡眠结构对于睡眠研究和诊断睡眠障碍至关重要.
研究的目的:
- 开发一种创新的人工神经网络模型,用于生成持续睡眠深度值 (SDV).
- 使用EEG数据的多功能融合方法,以提高睡眠分析的时间一致性.
- 提供比传统方法更细致的睡眠阶段表示.
主要方法:
- 收集了150名参与者的睡眠数据 (50个正常,100个OSAHS).
- 将数据细分为3秒间隔,提取38个特征.
- 采用集体随机森林来选择前7个与时间相关的特征.
- 训练了一个人工神经网络 (ANN) 模型来生成连续的SDV (0-1).
主要成果:
- 睡眠深度值 (SDV) 在清醒 (平均0.7021) 和深度睡眠阶段N3 (平均0.0396) 之间显著不同.
- 兴奋事件显示出明显的SDV增加 (0.1-0.3~0.7) 和随后的下降.
- 深度睡眠 (SDV ≤0.1) 与正常人 (<10%) 与OSAHS患者 (~30%) 的较低唤醒概率相关.
结论:
- 建议使用多特征融合生成SDV (0-1) 的新型睡眠连续性模型.
- 这个SDV模型捕捉了传统的睡眠阶段和微状态的变化.
- SDV模型是传统睡眠分阶段方法的有价值的补充或替代品.
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