睡眠呼吸暂停综合征患者脑电的去趋势波动分析

Detrended Fluctuation Analysis of Electroencephalogram of Patients with Sleep Apnea Syndrome

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 kind of harmful systemic sleep disorder with high incidence, and the pathological mechanism of it is complicated and the diagnosis and treatment are difficult. Mining the characteristic information of SAS from the single or small physiological signal is a hot topic in the research of sleep disorders in recent years. In our study shown in this paper, the detrended fluctuation analysis （DFA） was used to analyze sleep electroencephalogram （EEG） of SAS patients and normal healthy persons based on the non-stationary and nonlinear characteristics. It was found that in both groups, the scaling exponents increased gradually with the deepening of sleep, and in the rapid eye movement （REM） stage, the scaling exponents decreased. The scaling exponents of SAS group were significantly higher than those of the healthy group. The performance of SAS diagnosis based on scaling exponents was evaluated with receiver operator characteristic （ROC） curve. The optimal threshold value 0. 81 for the SAS and normal control were obtained, corresponding to the sensitivity 94.4%, specificity 99.2%, and area under curve （AUC） was 0. 994. The results show that DFA scaling exponents have a good discrimination power and accu- racy for the SAS, which provide a new theoretical basis for SAS diagnosis.