基于大脑皮层互信息理论的睡眠分级研究(英文)

Studying the Stages of Sleep based on the Mutual Information of Brain Cortex

睡眠的分级研究是睡眠状况分析和睡眠质量评价的前提和基本内容。目前国际通用的睡眠分级方法 ,是利用脑电信号另加脑功能信号 (如肌电图、眼动电流图 ) ,且必须由人工判别分析。大脑皮层互信息理论是研究脑功能变化的有力工具。通过动态计算睡眠脑电4个导联之间的互信息时间序列的复杂度 ,并利用一个 3层的人工神经网络进行 6个级别的分类。 6例 72 0个不同时期的睡眠片段的测试表明 ,系统睡眠分级与人工分级的总相符率达到 90 .83 % ,且实现了睡眠动态自动分级。神经网络的学习功能 ,可使系统的准确率进一步提高 ,逐渐接近或达到人工分级的水平。与其他睡眠分级方法相比 ,本系统有一定优势 ,且计算速度快 ,可望应用于临床实时睡眠监护及睡眠分析中。

A new approach to sleep analysis based on the mutual information of brain cortex is described. The mutual information time series among four leads were first computed using the EEG time series. The Lempel Ziv complexity measure, C(n)s , were extracted from the mutual information time series by complexity analysis. Sleep staging was then made by a three layer artificial neural network (ANN) using the C(n)s. The combination of these three different approaches enables the system to address the non analytical, non stationary, non linear and dynamical properties of the EEG. From 6 subject experiments, 720 distinct EEG epochs were used to test the results of sleep stage classification. The accuracy rate obtained for the system is 90 83%. Comparisons with other methods show that the proposed system has a certain advantage. Furthermore, the new method was computationally fast and well suited for real time clinical implementation.