睡眠脑电的非线性动力学方法

在8例健康成年人的睡眠脑电监测实验基础上，利用已有的专家人工分期结果，提取睡眠各阶段特征数据，应用近似熵、复杂度和功率谱熵三种方法进行分析，从客观量化的复杂性度量来刻划睡眠深度的变化情况，对每个睡眠分期选取5000点数据，数据窗取1000点，逐次延时一个采样间隔得到几个时间序列，分别求复杂度，最后取均值即得此分期复杂性测度值，结果表明三种方法均与专家人工分期结果相吻合，近似熵算法复杂不适合在线分析；复杂度算法较简单，但数据粗粒化处理容易丢失信息；功率谱熵算法简单、快速及有效，因而用统计分析方法分析，表明功率谱熵能较好地反映睡眠深度的变化情况。

EEG time-series analysis using nonlinear dynamics method for sleep monitoring

Approximate entropy, Lem-Ziv complexity and power spectral entropy(PSE) are nonlinear dynamic methods to measure Electroencephalograph (EEG) time-series complexity in recent years. EEG is a kind of biomedical signal to monitor the depth of sleep. The algorithm of approximate entropy, Lem-Ziv complexity and power spectral entropy are introduced. A discussion is made on their merits and (dismerits.) For 8 healthy volunteers without any medication, analysis of EEG using above (mentioned me-)thods is performed. Results show that the PSE of EEG signals during sleep process can correctly affect sleep deepness, and accord with the results of sleep stage by expert.