Cui Jian, Sun Yunliang, Jing Haifeng, Chen Qiang, Huang Zhihao, Qi Xin, Cui Hao
Department of Big Data and Fundamental Sciences, Shandong Institute of Petroleum and Chemical Technology, Dongying, Shandong, 257061, People's Republic of China.
Department of Respiratory and Sleep Medicine, Bin Zhou Medical University Hospital, Binzhou, Shandong, 256600, People's Republic of China.
Nat Sci Sleep. 2024 Jun 12;16:769-786. doi: 10.2147/NSS.S463897. eCollection 2024.
Sleep structure is crucial in sleep research, characterized by its dynamic nature and temporal progression. Traditional 30-second epochs falter in capturing the intricate subtleties of various micro-sleep states. This paper introduces an innovative artificial neural network model to generate continuous sleep depth value (SDV), utilizing a novel multi-feature fusion approach with EEG data, seamlessly integrating temporal consistency.
The study involved 50 normal and 100 obstructive sleep apnea-hypopnea syndrome (OSAHS) participants. After segmenting the sleep data into 3-second intervals, a diverse array of 38 feature values were meticulously extracted, including power, spectrum entropy, frequency band duration and so on. The ensemble random forest model calculated the timing fitness value for all the features, from which the top 7 time-correlated features were selected to create detailed sleep sample values ranging from 0 to 1. Subsequently, an artificial neural network (ANN) model was trained to delineate sleep continuity details, unravel concealed patterns, and far surpassed the traditional 5-stage categorization (W, N1, N2, N3, and REM).
The SDV changes from wakeful stage (mean 0.7021, standard deviation 0.2702) to stage N3 (mean 0.0396, standard deviation 0.0969). During the arousal epochs, the SDV increases from the range (0.1 to 0.3) to the range around 0.7, and decreases below 0.3. When in the deep sleep (≤0.1), the probability of arousal of normal individuals is less than 10%, while the average arousal probability of OSA patients is close to 30%.
A sleep continuity model is proposed based on multi-feature fusion, which generates SDV ranging from 0 to 1 (representing deep sleep to wakefulness). It can capture the nuances of the traditional five stages and subtle differences in microstates of sleep, considered as a complement or even an alternative to traditional sleep analysis.
睡眠结构在睡眠研究中至关重要，具有动态性和时间进程的特点。传统的30秒时间段在捕捉各种微睡眠状态的复杂细微之处时存在不足。本文介绍了一种创新的人工神经网络模型，用于生成连续睡眠深度值（SDV），该模型采用了一种新颖的多特征融合方法，结合脑电图（EEG）数据，并无缝整合了时间一致性。
该研究纳入了50名正常参与者和100名阻塞性睡眠呼吸暂停低通气综合征（OSAHS）患者。将睡眠数据分割为3秒的间隔后，精心提取了38种不同的特征值，包括功率、频谱熵、频段持续时间等。集成随机森林模型计算所有特征的时间适应度值，从中选择前7个与时间相关的特征，以创建范围从0到1的详细睡眠样本值。随后，训练了一个人工神经网络（ANN）模型来描绘睡眠连续性细节、揭示隐藏模式，并且远远超越了传统的五阶段分类（清醒、N1、N2、N3和快速眼动睡眠期）。
睡眠深度值从清醒阶段（平均值0.7021，标准差0.2702）变化到N3阶段（平均值0.0396，标准差0.0969）。在觉醒时间段内，睡眠深度值从（0.1至0.3）的范围增加到约0.7的范围，然后降至0.3以下。当处于深度睡眠（≤0.1）时，正常个体的觉醒概率小于10%，而OSA患者的平均觉醒概率接近30%。
提出了一种基于多特征融合的睡眠连续性模型，该模型生成范围从0到1的睡眠深度值（代表从深度睡眠到清醒）。它可以捕捉传统五个阶段的细微差别以及睡眠微状态的微妙差异，可被视为传统睡眠分析的补充甚至替代方法。
