OSA睡眠脑电的振荡与网络连接特征模式研究

发布时间：2018-05-11 10:50

  本文选题：阻塞性睡眠呼吸暂停低通气综合征 + 脑电　； 参考：《天津医科大学》2017年硕士论文

【摘要】：研究目的阻塞性睡眠呼吸暂停低通气综合征(Obstructive Sleep Apnea-hypopnea Syndrome,OSA)是一种睡眠障碍疾病,常伴随着心脑血管疾病的发生,严重损害患者的身心健康和认知功能。多导睡眠监测(Polysomnogram,PSG)是目前临床检测OSA的常用手段,但PSG在描述OSA睡眠障碍的内在机制方面存在局限性。因此,本论文在OSA患者PSG监测的基础上,以OSA多通道睡眠脑电(Electroencephalographs,EEGs)为研究对象,定量研究OSA患者在不同睡眠期EEGs神经振荡的异常模式,进而探索OSA睡眠EEGs网络的连接缺损机制,为OSA的临床诊断、治疗和发病机制研究提供新的研究思路和技术支持。研究方法1.受试者分组制定OSA入组标准,OSA组由10名符合入组标准的OSA患者组成,正常对照(Control)组由10名正常受试者组成,两组受试者在年龄、性别、教育程度上相匹配。所有受试者均来自天津医科大学总医院呼吸科睡眠诊疗中心。2.PSG指标两组受试者PSG监测指标如下:呼吸暂停低通气指数(Apnea/Hypopnea Index,AHI),氧饱和度低于90%的时间占监测时间的百分比(Percent of Time in Saturation Lower 90%,%TST90%),最低血氧饱和度(Minimal Pulse Oxygen Saturation,minSaO2),平均血氧饱和度(Average Oxyhemoglobin Saturation,meanSaO2),氧减指数(Oxygen Desaturation Index,ODI),微觉醒指数(Arousal Index,AI)。3.记录睡眠EEGs记录每名受试者在5个睡眠期:闭目清醒期(Wake Period,W)、睡眠Ⅰ期(Non-REM Sleep Stage 1,N1)、睡眠Ⅱ期(Non-REM Sleep Stage 2,N2)、睡眠Ⅲ期(Non-REM Sleep Stage 3,N3)和快速眼动期(Rapid-eye Movement Sleep Stage,R)有效的16通道EEGs数据,每期各10段,每段数据长为10 s。4.预处理原始记录EEGs数据原始记录EEGs数据去除基线漂移、工频干扰和肌电、眼电、心电造成的噪声和伪迹等。5.研究OSA睡眠EEGs振荡的特征模式应用快速傅里叶变换(Fast Fourier Transform,FFT)方法,计算OSA组和Control组在5个睡眠期EEGs各个频段(Delta、Theta、Alpha、Beta、Gamma)的平均能量密度,定量描述EEGs在各个频段的神经振荡。统计比较两组EEGs能量密度差异,研究OSA EEGs振荡的特征睡眠期、特征频段和特征脑区。6.研究OSA睡眠EEGs网络连接的特征模式应用频域Granger因果分析方法,计算OSA组和Control组在5个睡眠期EEGs各个频段的定向传递函数(Directed Transfer Function,DTF),定量描述EEGs在各个频段因果网络的连接强度。统计比较两组DTF连接值差异,研究OSA EEGs网络连接的特征睡眠期、特征频段和特征脑区。7.统计学分析应用SPSS 22.0统计分析软件对数据进行独立样本t检验,计量资料用均值±标准误(Mean±SEM)表示,相关性分析采用Pearson直线相关分析。研究结果1.OSA的PSG指标特征OSA的PSG监测结果显示全夜睡眠过程中的minSaO2、meanSaO2显著低于Control组,%TST90%、ODI指数及AI指数显著高于Control组,差异具有统计学意义(P0.05)。2.OSA睡眠EEGs振荡的特征模式(1)OSA在W期Delta频段,额区和中央区的EEGs能量密度比Control组显著增大(P0.01);在Alpha频段,顶区和枕区的EEGs能量密度比Control组显著减低(P0.05)。(2)OSA在N1期Delta频段,额区和中央区的EEGs能量密度比Control组显著增大(P0.01)。(3)OSA在N3期Delta频段和Theta频段,中央区的EEGs能量密度比Control组显著增大(P0.05)。3.OSA睡眠EEGs网络连接的特征模式(1)OSA在W期Gamma频段,额区的EEGs平均DTF值比Control组显著增大(P0.05);在Gamma频段中央区的EEGs平均DTF值比Control组显著减小(P0.05)。(2)OSA在N1期Beta频段,中央区的EEGs平均DTF值比Control组显著增大(P0.05)。(3)OSA在N3期Beta频段,中央区的EEGs平均DTF值比Control组显著增大(P0.05)。研究结论1.PSG监测结果提示OSA主要睡眠障碍表现为低氧损伤和睡眠结构紊乱。2.OSA发生睡眠障碍的特征睡眠期为W期、N1期和N3期。3.OSA在W期EEGs的神经振荡在Delta频段增强,在Alpha频段减弱,提示OSA患者在清醒时常处于思睡状态,脑电呈现慢波化,警觉水平下降;OSA在N1期、N3期EEGs的神经振荡在Delta和Theta频段增强,提示OSA在睡眠状态下的EEGs振荡发生慢波化改变,慢波抑制机制丧失,可能与OSA睡眠中唤醒反应能力下降,大脑觉醒功能损伤有关。4.OSA W期EEGs网络功能连接在Gamma频段增强,提示OSA患者清醒状态下脑功能连接代偿性增强,可能为维持注意、警觉等正常脑功能所做的代偿性调节;OSA N1期、N3期EEGs网络功能连接在Beta频段增强,可能与睡眠呼吸暂停过程中大脑对呼吸肌的调节控制努力有关,反映了OSA睡眠状态下为弥补感觉运动功能缺陷的代偿机制。5.OSA发生睡眠EEGs振荡异常与网络连接异常的脑区主要集中在额区和中央区,提示OSA睡眠EEGs模式的改变可能与其额区、中央区的结构和功能损伤有关。
[Abstract]:Objective obstructive sleep apnea hypopnea syndrome (Obstructive Sleep Apnea-hypopnea Syndrome, OSA) is a kind of sleep disorder, often accompanied by the occurrence of cardiovascular and cerebrovascular diseases, which seriously damage the physical and mental health and cognitive function of the patients. Polysomnogram (PSG) is a common means of clinical detection of OSA at present. But PSG has limitations in describing the internal mechanism of OSA sleep disorder. Therefore, on the basis of PSG monitoring in OSA patients, this paper uses OSA multichannel sleep electroencephalogram (Electroencephalographs, EEGs) as the research object to quantitatively study the abnormal mode of EEGs nerve ringing in OSA patients during different sleep periods, and then explore the connection of OSA sleep EEGs network. The mechanism of defect connection provides new research ideas and technical support for the clinical diagnosis, treatment and pathogenesis of OSA. Methods 1. subjects were divided into groups to set up a standard of OSA group, group OSA was composed of 10 OSA patients who met the standard of entry group, and the normal control group (Control) was composed of 10 normal subjects, and the two subjects were in age, sex, education. All subjects were from the two group of.2.PSG indicators in the sleep diagnosis center of General Hospital Affiliated to Tianjin Medical University Department of respiration. The PSG monitoring indexes of the two groups were as follows: the apnea hypopnea index (Apnea/Hypopnea Index, AHI), the percentage of oxygen saturation less than 90% (Percent of Time in Saturation Lower 90%,%) TST90%), the lowest blood oxygen saturation (Minimal Pulse Oxygen Saturation, minSaO2), the average blood oxygen saturation (Average Oxyhemoglobin Saturation, meanSaO2), the oxygen subtraction index (Oxygen Desaturation), the micro awakening index recorded in the 5 sleep periods of each subject. ), sleep phase I (Non-REM Sleep Stage 1, N1), sleep stage II (Non-REM Sleep Stage 2, N2), sleep stage III (Non-REM Sleep Stage 3, N3) and rapid eye movement 16 channel data, each period of 10 segments, each segment of the data is 10 Baseline drift, frequency interference and EMG, electromyography, noise and artifacts caused by electrocardiography, the.5. study of the characteristic patterns of OSA sleep EEGs oscillations using the fast Fourier transform (Fast Fourier Transform, FFT) method to calculate the average energy density of OSA and Control groups at each of the 5 sleep periods (Delta, Theta, etc.). The neural oscillations of EEGs in each frequency band. Compare the difference of the two groups of EEGs energy density, study the characteristic sleep period of the OSA EEGs oscillation, the characteristic frequency and the characteristic brain region.6. study OSA sleep EEGs network connection characteristic pattern using the frequency domain Granger causality analysis method, and calculate the orientation of the OSA group and Control group in the 5 sleep EEGs frequency bands. Directed Transfer Function (DTF), quantificationally describe the connection strength of EEGs in each frequency and effect network, compare the difference between two groups of DTF connection values, study the characteristic sleep period of OSA EEGs network connection, the characteristic frequency and the statistical analysis of the characteristic brain region, and apply the SPSS 22 statistical analysis software to carry out the independent sample t test of the data. The measurement data were expressed with mean standard error (Mean + SEM), and correlation analysis used Pearson linear correlation analysis. The results of PSG monitoring of OSA of PSG index of 1.OSA showed that meanSaO2 was significantly lower than Control group and%TST90%, and the ODI index and index index were significantly higher than those in the group, and the difference was statistically significant. The characteristic mode of EEGs oscillation of P0.05.2.OSA sleep (1) OSA in W phase Delta band, EEGs energy density in the frontal and central regions is significantly higher than that of Control group (P0.01), and the EEGs energy density in the Alpha band, the top and the occipital region is significantly lower than that of the Control group. (2) the energy density of the frontal and central regions is more than that of the Control. The group was significantly increased (P0.01). (3) OSA in the N3 phase Delta band and Theta band, the EEGs energy density in the central region was significantly higher than that in the Control group (P0.05) in the.3.OSA sleep EEGs network connection model (1) OSA in the W phase, and the average value of the frontal region was significantly higher than that of the Control group. Group ol decreased significantly (P0.05). (2) the average EEGs DTF value in the central region was significantly higher than that of the Control group (P0.05) at the Beta band of the N1 phase (P0.05). (3) OSA was in N3 Beta band, and the mean value of the central region was significantly higher than that of the Control group. The characteristic sleep period of SA is W phase, and the nervous oscillation of EEGs in phase N1 and N3 phase is enhanced at the Delta frequency, and the Alpha frequency is weakened. It suggests that the OSA patients are often in the sleep state, the electroencephalogram presents slow wave, and the alert level drops. The EEGs oscillation in the state of sleep may change slowly, the mechanism of slow wave inhibition is lost, and the arousal response ability in OSA sleep may be reduced. The impairment of the brain awakening function is related to the enhancement of the Gamma band in the EEGs network of the.4.OSA W phase, suggesting that the brain function connection is compensatory in the sober state of the OSA patients, which may be the maintenance of attention and vigilance. Compensatory regulation such as normal brain function; OSA N1 phase, N3 phase EEGs network function connection is enhanced in the Beta band, may be related to the control efforts of the brain in the process of sleep apnea, reflecting the compensatory mechanism of OSA sleep to compensate for the dysfunction of sensory motor function.5.OSA to occur sleep EEGs oscillation abnormal and net The abnormal cerebral regions of the collaterals are mainly concentrated in the frontal and central areas, suggesting that the changes in the EEGs pattern of OSA sleep may be related to the structural and functional impairment in the central area.

【学位授予单位】：天津医科大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R766

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