睡眠脑电的自回归模型阶数特性

Autoregressive Model Order Property for Sleep EEG

传统睡眠脑电 (Sleep EEG)研究从信号的时域和频域的特征分析睡眠过程 ,通常根据功率谱观察信号中特定节律的出现和频带的分布。而功率谱估计中基于参数模型的方法得到广泛应用 ,但建模时通常只能根据经验选择一个固定较低的阶数。本文讨论了自回归模型阶数 (Autoregressive m odel order,ARMO)估计准则的一些最新进展 ,并且统计了一段睡眠过程中 EEG的阶数分布。结果显示 EEG的 ARMO分布集中在差别很大的几个区间 ,可以用来表示睡眠 EEG分期内微结构和过渡过程 ,并在一定程度上提供

Traditional sleep scoring system describes the sleep EEG characterized by features in time domain as well as frequency domain. Power Spectral Density (PSD) is one of the well-used methods to observe the occurrence of specified rhythms. However, the parameter model based PSD estimation is used with the assumption that the model order is determined as low as possible through prior knowledge. This paper briefs the development of Autoregressive Model Order (ARMO) criterion, and provides the distribution of ARMOs for specified sleep EEG, which shows that ARMOs concentrate on several well separated regions that are indicative of the microstructure and transition states. This study suggests the promising perspective of ARMO as a special EEG feature for weighing complexity, randomness and rhythm components.