基于BP网络的睡眠分阶方法及睡眠质量评估研究

我们利用不同睡眠期脑电复杂性特征与睡眠深度的关系及多道睡眠图功率谱特征,基于3层BP网络进行了睡眠自动分阶的研究,并提出了能部分反映睡眠质量的睡眠时间、浅睡时间、深睡时间、REM时间、觉睡比、醒转次数等参数。通过6例全睡眠监护实验说明,该方法可为睡眠质量的评价提供途径。     

基于小波分析提取心动周期中的睡眠呼吸事件信息

为了挖据心动周期中的睡眠呼吸事件信息,为无干扰睡眠呼吸事件监测提供技术支持,本文利用多分辨率小波分析方法,对患有睡眠呼吸暂停低通气综合征病人的心率序列进行分解和重建,获得与睡眠呼吸事件相关联的特征波形.根据特征波形的波形特点与睡眠呼吸事件的关系,最后识别出呼吸事件发生的位置和类型.上述方法的分析结果与多功能睡眠记录仪结果进行比较证实该方法是有效和可行的.     

基于小波分析提取心动周期中睡眠呼吸事件信息

为了挖掘心动周期中睡眠呼吸事件信息,为无干扰监测睡眠呼吸事件提供技术支持,本文利用多分辨率小波分析方法,对睡眠呼吸暂停低通气综合征病人的整晚心率序列进行分解和重建,获得与睡眠呼吸事件相关联的特征波形;再根据特征波形的波形特点与睡眠呼吸事件的关系,识别呼吸事件发生的位置和类型.本方法提取的试验结果与标准睡眠图仪提示的结果进行比较表明,基于小波分析提取心动周期中睡眠呼吸事件信息的方法是有效和可行的.     

多分辨率小波信号分解用于大鼠睡眠纺锤波的分析

本研究首先设计了慢波睡眠期脑电信号的合成仿真信号 ,对小波基函数进行了选择 ,结果证明Coiflet 5阶小波变换对大鼠慢波睡眠期EEG信号具有较好的分解结果。据此 ,应用多分辨率小波分析法设计了提取睡眠纺锤波的算法 ,并利用该算法对安定用药后和睡眠剥夺后大鼠慢波睡眠期纺锤波的持续时间和能量变化进行了分析 ,结果表明 :安定具有延长慢波睡眠期纺锤波持续时间的作用 ,而睡眠剥夺可以增加慢波睡眠期纺锤波的能量。这些结果说明 ,小波分析算法可以提供功率谱分析无法表现的时频信息。     

穿戴式呼吸感应体积描记用于睡眠呼吸事件检测

可穿戴式呼吸感应体积描记(背心式RIP)系统是我们根据呼吸感应体积描记技术的基本原理研发的一种可穿戴、低负荷的呼吸监测系统.在实现通气量无创测量的基础上,我们将该系统用于睡眠期呼吸事件检测,将该系统与多导睡眠图仪(PSG)对9例疑似睡眠呼吸暂停低通气综合症(SAHS)病人和7名健康男性志愿者进行同步对照检测与分析.通过对比实验,根据背心式RIP系统发生呼吸事件的特征性变化,提出了背心式RIP系统判别呼吸事件的规则.依据该规则,所有经背心式RIP系统诊断为SAHS患者的结果与PSG的诊断结果完全一致,背心式RIP系统检测呼吸事件的敏感性为97.8%,特异性为95.8%,实验结果表明背心式RIP系统能够可靠地检测出睡眠呼吸事件.由于其低生理、心理负荷特性,不需要佩带口鼻气流传感器,可用于家庭环境下、自然睡眠过程的睡眠呼吸紊乱性疾病的诊断.     

睡眠自动分阶现状

睡眠障碍已成为临床医学和人类生活中普遍关心的问题.关于整夜睡眠状况的分析及睡眠质量的评价是睡眠生理研究中有重要实用价值的课题.而睡眠分阶则是整夜睡眠状况的分析及睡眠质量的评价的基础.长期以来睡眠分阶是由人工实现的.人工分阶一般具有费时,带有很强的主观性等缺点.因此,睡眠脑电自动分阶成为很长一段时间以来的热点.     

OSAHS患者自然睡眠期呼吸模式对上气道流场的影响

目的分析阻塞性睡眠呼吸暂停低通气综合征(obstructive sleep apnea hypopnea syndrome,OSAHS)患者自然睡眠时平静呼吸和呼吸暂停期不同压力边界条件和呼吸模式对气道内气体的流动和生理状态的影响。方法创建OSAHS患者仰卧位自然睡眠状态,并采集CT数据建立三维上气道有限元模型。临床测量患者睡眠期喉腔压力作为边界条件,考虑鼻吸鼻呼、鼻吸口呼、口吸鼻呼、口吸口呼4种典型呼吸模式进行流体力学仿真。结果睡眠期OSAHS患者的呼吸气流呈非稳定、有涡、双向流动,压力边界以及呼吸模式对气体流动的影响明显。用口呼吸与用鼻呼吸相比,气体的最大流速有所升高,压降主要集中在口腔,吸气时升高约30%,呼气时升高1倍。结论采用OSAHS患者自然睡眠期CT数据建模并以临床喉腔压力作为边界条件进行有限元仿真具有意义,研究结果有助于了解OSAHS患者真实自然睡眠状态下的上气道流场特性。     

基于光电容积脉搏波特征信息的睡眠呼吸事件判别

研究脉搏波特征信息与睡眠呼吸事件的关系,实现对呼吸事件的初步判别。采用小波变换的方法分析光电容积脉搏波,提取特征参量,结合专家经验校正后的呼吸事件分布,寻找这些参量与睡眠呼吸事件的相关关系,确立呼吸事件的初步判据,并用于呼吸事件初步判别。研究了5位受试者夜间脉搏波信号,专家校正的一晚呼吸事件总数为1239次。结果表明,呼吸事件初步判别的符合率在91%以上,尤其对于患有中重度OSAHS的病人,符合率可达96.1%。本研究提供了利用脉搏波特征参量进行呼吸事件初步判别的理论根据和实现方法。     

睡眠呼吸监测技术的研究进展

睡眠呼吸监测技术对于睡眠呼吸暂停综合征的预防、发现及治疗起着重要作用.简要介绍用于睡眠呼吸暂停综合征的监测设备分级及应用趋势,分析睡眠呼吸监测技术的特点,对睡眠呼吸监测技术的研究方向和发展现状进行了综述.     

多项睡眠图联合长程视频脑电图观察未治疗的28例癫癎患者睡眠结构和呼吸事件的变化

目的:探讨成人癫癎患者药物治疗前睡眠结构和睡眠呼吸事件的变化。方法:对确诊为癫癎的成人患者28例进行多项睡眠图(PSG)检查,同步行长程视频脑电图(LTV EEG)检查,分析患者的睡眠结构、睡眠呼吸事件情况。结果:本组病人PSG睡眠结构特点表现为各睡眠参数均有不同程度的改变,以REM潜伏期增加、REM睡眠减少为著,浅睡眠明显增多,而深睡眠则无明显差异。其中2例患者无REM睡眠。所有患者夜间觉醒次数均增多,睡眠效率明显下降。但在觉醒时间、周期性腿动和呼吸暂停指数上,并无明显变化。结论:癫癎患者在药物治疗前存在睡眠结构紊乱。     

睡眠呼吸暂停综合征患者的睡眠分期算法研究

针对可穿戴睡眠监测缺乏有效的自动睡眠分期和睡眠质量评价方法这一问题,提出一种适用于睡眠呼吸暂停综合征患者的自动睡眠分期方法。通过心电图R-R间期序列,分别得到心率变异性、呼吸幅度变异性和呼吸率变异性信号。以此为基础,提取时域、频域及非线性特征共55个。利用门控循环单元网络,分别构建清醒-睡眠二分类、清醒-快速眼动-非快速眼动睡眠三分类、清醒-快速眼动-浅睡-慢波睡眠四分类、清醒-快速眼动-非快速眼动Ⅰ-Ⅱ-Ⅲ期五分类等共4个不同分类粒度的睡眠分期模型;采用损失函数类别加权方法,有效降低数据非平衡对分期结果的影响。验证数据来自SHRS数据库的274例患者。借助准确率、Cohen's Kappa系数和睡眠结构指标对该睡眠分期方法进行性能评价。结果表明4个分类器的准确率分别为85.06%、75.44%、63.80%、62.13%,Cohen's Kappa系数达到了0.54、0.49、0.41、0.41,睡眠结构分析评估与临床结果之间的差异无统计学意义。所提出的方法基本满足睡眠质量评估的需求,适用于可穿戴睡眠监测应用。     

智能实时睡眠监测与干预系统的设计与实现

目的设计可以监测、分析、干预以及反馈人体睡眠状况的智能实时睡眠监测与干预系统,以改善使用者睡眠问题。方法首先设计智能实时睡眠监测与干预系统,其中生命体征信号采集模块实时监测人体肢体动作、心脏泵血等引发的振动;数据处理中心提取体动信号、呼吸信号与心冲击信号(ballistocardiogram,BCG)信号,利用睡眠监测算法进行睡眠状况分析;中药香薰器由数据处理中心控制开关,利用含有中药成分的香薰干预睡眠;同时睡眠监测实时反馈干预情况下的数据。然后选取3名失眠的被试者进行测试,检验系统的有效性。结果所构建的系统可以实时记录被试者睡眠情况并形成睡眠分期图,也可利用中药香薰有效地干预被试者不良睡眠情况。结论该系统能够实现非接触式睡眠情况采集与分析以及对入睡困难或睡眠质量不佳等状况的干预。     

Modelling and exploring human sleep with event history analysis

In this paper we propose the use of statistical models of event history analysis for investigating human sleep. These models provide appropriate tools for statistical evaluation when sleep data are recorded continuously over time or on a fine time grid, and are classified into sleep stages such as REM and nonREM as defined by Rechtschaffen and Kales (1968). In contrast to conventional statistical procedures, event history analysis makes full use of the information contained in sleep data, and can therefore provide new insights into non-stationary properties of sleep. Probabilities of or intensities for transitions between sleep stages are the basic quantities for characterising sleep processes. The statistical methods of event history analysis aim at modelling and estimating these intensities as functions of time, taking into account individual sleep history and assessing the influence of factors of interest, such as hormonal secretion. In this study we suggest the use of non-parametric approaches to reveal unknown functional forms of transition intensities and to explore time-varying and non-stationary effects. We then apply these techniques in a study of 30 healthy male volunteers to assess the mean population intensity and the effects of plasma cortisol concentration on the transition between selected sleep stages as well as the influence of elapsed time in a current REM period on the intensity for a transition to nonREM. The most interesting findings are that (a) the intensity of the nonREM-to-REM transitions after sleep onset in young men shows a periodicity which is similar to that of nonREM/REM cycles; (b) 30-45 min after sleep onset, young men reveal a great propensity to pass from light sleep (stages 1 or 2) into slow-wave sleep (SWS) (stages 3 or 4); (c) high cortisol levels imposed additional impulses on the transition intensity of (i) wake to sleep around 2 h after sleep onset, (ii) nonREM to REM around 6 h later, (iii) stage 1 or stage 2 sleep to SWS around 2, 4 and 6 h later and (iv) SWS to stage 1 or stage 2 sleep about 2 h later. Moreover, high cortisol concentrations at the beginning of REM periods favoured the change to nonREM sleep, whereas later their influence on a nonREM change became weak and weaker. As sleep data are also available as event-oriented data in many studies in sleep research, event history analysis applied additionally to conventional statistical procedures, such as regression analysis or analysis of variance, could help to acquire more information and knowledge about the mechanisms behind the sleep process.     

A novel sleep stage scoring system: Combining expert‐based features with the generalized linear model

In this study, we aim to automate the sleep stage scoring process of overnight polysomnography (PSG) data while adhering to expert‐based rules. We developed a sleep stage scoring algorithm utilizing the generalized linear modelling (GLM) framework and extracted features from electroencephalogram (EEG), electromyography (EMG) and electrooculogram (EOG) signals based on predefined rules of the American Academy of Sleep Medicine (AASM) Manual for Scoring Sleep. Specifically, features were computed in 30‐s epochs in the time and frequency domains of the signals and were then used to model the probability of an epoch being in each of five sleep stages: N3, N2, N1, REM or Wake. Finally, each epoch was assigned to a sleep stage based on model predictions. The algorithm was trained and tested on PSG data from 38 healthy individuals with no reported sleep disturbances. The overall scoring accuracy reached on the test set was 81.50 ± 1.14% (Cohen's kappa, ). The test set results were highly comparable to the training set, indicating robustness of the algorithm. Furthermore, our algorithm was compared to three well‐known commercialized sleep‐staging tools and achieved higher accuracies than all of them. Our results suggest that automatic classification is highly consistent with visual scoring. We conclude that our algorithm can reproduce the judgement of a scoring expert and is also highly interpretable. This tool can assist visual scorers to speed up their process (from hours to minutes) and provides a method for a more robust, quantitative, reproducible and cost‐effective PSG evaluation, supporting assessment of sleep and sleep disorders.     

基于多尺度深度网络的自动睡眠分期

传统睡眠质量评估与诊断高度依赖医生的经验以及对长时间睡眠监测数据的分析统计,耗时耗力,且传统机器学习技术所实现的自动睡眠分期依赖人工构造的特征,在发掘深层次分期特征上效果有限,对部分分期的辨识效果欠佳.提出一种基于多尺度深度网络(MSDNet)的自动睡眠分期算法,能够自动分析提取睡眠信号特征,并基于不同睡眠阶段的分期难...     

睡眠分期的符号转移熵分析

目的睡眠分期是衡量睡眠质量和诊治睡眠障碍性疾病的重要途径,转移熵是一个量化2个序列相关程度的参数。本文将基于符号化技术的符号转移熵首次应用在睡眠分期研究中,克服了以往方法对参数之间协调性要求高以及对噪声敏感的缺点。方法通过提取相同个体相同时刻的清醒期和非快速眼动睡眠I期的EEG、ECG信号,分别进行符号化、相空间重构后,计算符号转移熵,对两个睡眠阶段的符号转移熵进行t检验及多样本验证。结果实验结果表明清醒期符号转移熵大于非快速眼动睡眠I期的符号转移熵。经t检验表明这两个阶段的符号转移熵值有显著性差异,并通过多样本验证。随着睡眠加深,身体单元不断偶合,符号转移熵减小,与理论分析相符合。结论清醒期和非快速眼动睡眠I期的符号转移熵很好地体现了睡眠状态的变化,因此符号转移熵可用于睡眠分期,并成为研究睡眠自动化分期的极具潜力的分析工具。     

基于心率变异性分析的睡眠分期算法研究和验证

为实现睡眠分期,为穿戴式生理参数监测技术在慢病监测领域的应用提供技术支撑,发展基于心率变异性和支持向量机模型的睡眠分期算法。从心率时间间期序列中提取时域、频域和非线性等86个特征,将多导睡眠图仪的三分类结果(醒、快速眼动期、非快速眼动期)作为“金标准”,采用支持向量机作为多分类器模型;为保证训练集数据质量,使用开放睡眠数据库SHHS中由专家确认挑选的67例PSG样本作为训练集,实现特征筛选和模型参数训练。为验证模型的泛化性能,从SHHS数据库中进一步随机提取939例PSG样本,对模型性能进行测试。睡眠分期模型在训练集上的五折交叉验证的准确率为84.00%±1.33%,卡帕系数为0.70±0.03;在939例测试集上的准确率为76.10%±10.80%,卡帕系数为0.57±0.15。剔除RR间期异常(110例)和明显睡眠结构异常(29例)的样本后,测试集(800例)的准确率为82.00%±5.60%,卡帕系数为0.67±0.14。所提出的基于心率变异性分析的睡眠分期算法具有较高的准确性,大样本人群测试结果表明,该模型具有较好的普适性。     

Automated Sleep Apnea Quantification Based on Respiratory Movement

Obstructive sleep apnea (OSA) is a prevalent and treatable disorder of neurological and medical importance that is traditionally diagnosed through multi-channel laboratory polysomnography(PSG). However, OSA testing is increasingly performed with portable home devices using limited physiological channels. We tested the hypothesis that single channel respiratory effort alone could support automated quantification of apnea and hypopnea events. We developed a respiratory event detection algorithm applied to thoracic strain-belt data from patients with variable degrees of sleep apnea. We optimized parameters on a training set (n=57) and then tested performance on a validation set (n=59). The optimized algorithm correlated significantly with manual scoring in the validation set (R² = 0.73 for training set, R² = 0.55 for validation set; p<0.05). For dichotomous classification, the AUC was >0.92 and >0.85 using apnea-hypopnea index cutoff values of 5 and 15, respectively. Our findings demonstrate that manually scored AHI values can be approximated from thoracic movements alone. This finding has potential applications for automating laboratory PSG analysis as well as improving the performance of limited channel home monitors.     

Sleep respiratory disturbances and arousals at moderate altitude have overlapping electroencephalogram spectral signatures

An ascent to altitude has been shown to result in more central apneas and a shift towards lighter sleep in healthy individuals. This study employs spectral analysis to investigate the impact of respiratory disturbances (central/obstructive apnea and hypopnea or periodic breathing) at moderate altitude on the sleep electroencephalogram (EEG) and to compare EEG changes resulting from respiratory disturbances and arousals. Data were collected from 51 healthy male subjects who spent 1 night at moderate altitude (2590 m). Power density spectra of Stage 2 sleep were calculated in a subset (20) of these participants with sufficient artefact‐free data for (a) epochs with respiratory events without an accompanying arousal, (b) epochs containing an arousal and (c) epochs of undisturbed Stage 2 sleep containing neither arousal nor respiratory events. Both arousals and respiratory disturbances resulted in reduced power in the delta, theta and spindle frequency range and increased beta power compared to undisturbed sleep. The similarity of the EEG changes resulting from altitude‐induced respiratory disturbances and arousals indicates that central apneas are associated with micro‐arousals, not apparent by visual inspection of the EEG. Our findings may have implications for sleep in patients and mountain tourists with central apneas and suggest that respiratory disturbances not accompanied by an arousal may, none the less, impact sleep quality and impair recuperative processes associated with sleep more than previously believed.     

Photoplethysmography as a single source for analysis of sleep-disordered breathing in patients with severe cardiovascular disease

Sleep-disordered breathing and Cheyne-Stokes breathing are often not diagnosed, especially in cardiovascular patients. An automated system based on photoplethysmographic signals might provide a convenient screening and diagnostic solution for patient evaluation at home or in an ambulatory setting. We compared event detection and classification obtained by full polysomnography (the 'gold standard') and by an automated new algorithm system in 74 subjects. Each subject underwent overnight polysomnography, 60 in a hospital cardiology department and 14 while being tested for suspected sleep-disordered breathing in a sleep laboratory. The sleep-disordered breathing and Cheyne-Stokes breathing parameters measured by a new automated algorithm system correlated very well with the corresponding results obtained by full polysomnography. The sensitivity of the Cheyne-Stokes breathing detected from the system compared to full polysomnography was 92% [95% confidence interval (CI): 78.6-98.3%] and specificity 94% (95% CI: 81.3-99.3%). Comparison of the Apnea Hyponea Index with a cutoff level of 15 shows a sensitivity of 98% (95% CI: 87.1-99.6%) and specificity of 96% (95% CI: 79.8-99.3%). The detection of respiratory events showed agreement of approximately 80%. Regression and Bland-Altman plots revealed good agreement between the two methods. Relative to gold-standard polysomnography, the simply used automated system in this study yielded an acceptable analysis of sleep- and/or cardiac-related breathing disorders. Accordingly, and given the convenience and simplicity of its application, this system can be considered as a suitable platform for home and ambulatory screening and diagnosis of sleep-disordered breathing in patients with cardiovascular disease.