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睡眠呼吸暂停综合征(SAS)是一种发病率高且危害巨大的睡眠疾病,其病理机制复杂,诊治困难,从单一或少量生理信号中挖掘SAS的特征信息是近年来睡眠疾病研究的热点。本文基于脑电(EEG)的非平稳和非线性特性,采用去趋势波动分析(DFA)对SAS患者和健康人的睡眠脑电进行研究。研究发现,SAS患者和健康人睡眠脑电的标度指数α随着睡眠加深逐渐增大,而在快速眼动期(REM)则下降;与此同时,SAS组的标度指数在各个睡眠阶段均高于对照组,两组间存在明显差异(P<0.01);采用受试者工作特征(ROC)曲线对脑电标度指数区分SAS的性能进行评价,得到SAS组和对照组的睡眠脑电标度指数最佳临界值0.81,对应灵敏度为94.4%,特异度为99.2%,曲线下面积(AUC)为0.994。结果说明DFA标度指数用于SAS区分有很好的辨别能力和准确度,为SAS诊断提供了新的理论依据。
Sleep apnea syndrome (SAS) is a sleep disease with high incidence and great harm. Its pathological mechanism is complex, diagnosis and treatment is difficult. Mining SAS features from single or small amount of physiological signal is a hot research topic in recent years. Based on the non-stationary and non-linear characteristics of EEG, this study investigated sleep-onset EEG in SAS patients and healthy subjects by using De-Trend Volatility Analysis (DFA). The study found that in patients with SAS and healthy people, the scale index of sleep EEG gradually increased with the deepening of sleep and decreased in REM. At the same time, the scale index of SAS group in each sleep stage (P <0.01). The ROC curve was used to evaluate the performance of SAS in distinguishing SAS from the control group, and the sleep brain of SAS group and control group was obtained The best cut-off value of electrical scale index was 0.81, corresponding to a sensitivity of 94.4%, a specificity of 99.2% and an area under the curve (AUC) of 0.994. The results showed that the DFA scale index used for the discrimination of SAS has good discrimination ability and accuracy, which provides a new theoretical basis for SAS diagnosis.