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睡眠呼吸暂停综合征(SAS)是一种常见且危害巨大的全身性睡眠疾病。SAS患者存在明显的脑部结构和功能的影像学改变,而脑电图(EEG)能反映大脑组织的电活动及功能状态,是描述睡眠过程最直观的参数。基于EEG信号的非平稳和非线性特性,本文采用非线性方法对SAS患者睡眠EEG信号的关联维特性进行分析。将6名SAS患者组成SAS组,6名健康人组成对照组。研究结果显示,SAS患者和健康人睡眠EEG信号的关联维变化规律一致,即随着睡眠加深,其关联维均逐渐减小,但到快速眼动期(REM)时,关联维又上升至觉醒和浅睡眠期的水平;与此同时,SAS组的关联维在各个睡眠阶段均低于对照组,两组间存在的差异具有统计学意义(P<0.01)。研究结果表明,SAS患者的EEG信号与健康人之间存在明显的非线性动力学差异,这为研究SAS的生理机制及实现SAS的自动检测提供了新的方向。
Sleep apnea syndrome (SAS) is a common and devastating systemic sleep disorder. SAS patients have obvious imaging changes of brain structure and function, and EEG can reflect the electrical activity and functional status of brain tissue. It is the most intuitive parameter to describe sleep process. Based on the non-stationary and non-linear characteristics of EEG signals, this paper uses non-linear methods to analyze the correlation dimensionality characteristics of sleep EEG signals in SAS patients. Six SAS patients were included in the SAS group and six healthy individuals were included in the control group. The results showed that the association of sleep EEG signal changes in patients with SAS and healthy subjects was consistent, that is, with the deepening of sleep, the correlation dimension decreased gradually, but in the REM, the correlation dimension increased again to wake up At the same time, the correlation dimension in SAS group was lower than that in control group at each sleep stage, and the difference between the two groups was statistically significant (P <0.01). The results show that SAS patients with EEG signals and healthy people there is a significant nonlinear dynamics difference between the SAS for the study of the physiological mechanisms and to achieve the SAS automatic detection provides a new direction.