A Novel Continuous Sleep State Artificial Neural Network Model Based on Multi-Feature Fusion of Polysomnographic Data

IF 3 2区医学 Q2 CLINICAL NEUROLOGY

Nature and Science of Sleep Pub Date : 2024-06-01 DOI:10.2147/nss.s463897

Jian Cui, Yunliang Sun, Haifeng Jing, Qiang Chen, Zhihao Huang, Xin Qi, Hao Cui

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Abstract

Purpose: 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. Methods: 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). Results: 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%. Conclusion: 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.

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基于多特征融合多导睡眠图数据的新型连续睡眠状态人工神经网络模型

目的：睡眠结构在睡眠研究中至关重要，其特点是动态性和时间进展性。传统的 30 秒纪元无法捕捉到各种微睡眠状态的复杂微妙之处。本文介绍了一种创新的人工神经网络模型，利用新颖的多特征融合方法与脑电图数据无缝整合，生成连续的睡眠深度值（SDV）。研究方法研究涉及 50 名正常人和 100 名阻塞性睡眠呼吸暂停-低通气综合征（OSAHS）患者。将睡眠数据分割成 3 秒钟的时间间隔后，细致地提取了 38 个不同的特征值，包括功率、频谱熵、频带持续时间等。随后，通过训练人工神经网络（ANN）模型来划分睡眠的连续性细节，揭示隐藏的模式，远远超越了传统的五阶段分类法（W、N1、N2、N3 和 REM）。结果SDV 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）。在唤醒期，SDV 从（0.1 至 0.3）上升到 0.7 左右，并在 0.3 以下下降。当处于深度睡眠（≤0.1）时，正常人的唤醒概率低于 10%，而 OSA 患者的平均唤醒概率接近 30%。结论本文提出了一种基于多特征融合的睡眠连续性模型，该模型可生成从 0 到 1 的 SDV（代表深度睡眠到清醒）。它能捕捉到传统五个阶段的细微差别和睡眠微观状态的细微差别，可作为传统睡眠分析的补充甚至替代。

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来源期刊

Nature and Science of Sleep Neuroscience-Behavioral Neuroscience

CiteScore

5.70

自引率

5.90%

发文量

245

审稿时长

16 weeks

期刊介绍： Nature and Science of Sleep is an international, peer-reviewed, open access journal covering all aspects of sleep science and sleep medicine, including the neurophysiology and functions of sleep, the genetics of sleep, sleep and society, biological rhythms, dreaming, sleep disorders and therapy, and strategies to optimize healthy sleep. Specific topics covered in the journal include: The functions of sleep in humans and other animals Physiological and neurophysiological changes with sleep The genetics of sleep and sleep differences The neurotransmitters, receptors and pathways involved in controlling both sleep and wakefulness Behavioral and pharmacological interventions aimed at improving sleep, and improving wakefulness Sleep changes with development and with age Sleep and reproduction (e.g., changes across the menstrual cycle, with pregnancy and menopause) The science and nature of dreams Sleep disorders Impact of sleep and sleep disorders on health, daytime function and quality of life Sleep problems secondary to clinical disorders Interaction of society with sleep (e.g., consequences of shift work, occupational health, public health) The microbiome and sleep Chronotherapy Impact of circadian rhythms on sleep, physiology, cognition and health Mechanisms controlling circadian rhythms, centrally and peripherally Impact of circadian rhythm disruptions (including night shift work, jet lag and social jet lag) on sleep, physiology, cognition and health Behavioral and pharmacological interventions aimed at reducing adverse effects of circadian-related sleep disruption Assessment of technologies and biomarkers for measuring sleep and/or circadian rhythms Epigenetic markers of sleep or circadian disruption.