Schäfer T, Porzler C, Schläfke M E
Abteilung für angewandte Phystologie, Ruhr-Universität Bochum, Deutschland.
Wien Med Wochenschr. 1996;146(13-14):377-8.
We used a laboratory software package for data capture and data analysis (Spike 2, CED) to develop a computerised sleep staging. The modular software is programmed to extract the following data: power spectral density in the alpha and delta frequency bands with fast Fourier Transformation, K-complexes using pattern recognition on the basis of signal amplitude and zero level crossings, sleep spindles using auto-correlation of the EEG signal, rapid eye movements with pattern recognition of the bipolar EOG signal using amplitude and time differences, and muscle tone from the rectified and integrated submental EMG signal. These results are displayed together with the raw signals of EEG, EOG, and chin EMG on a user-defined time scale. Thus visual scoring of sleep stages is easter. Furthermore a decision table, containing the sequence and weighting factors for the extracted parameters, serves to perform an automated sleep scoring.
我们使用了一个用于数据采集和数据分析的实验室软件包（Spike 2，CED）来开发计算机化睡眠分期。该模块化软件被编程用于提取以下数据：通过快速傅里叶变换得到的α和δ频段的功率谱密度、基于信号幅度和过零检测利用模式识别得到的K复合波、利用脑电图信号的自相关得到的睡眠纺锤波、利用双极眼电图信号的幅度和时间差进行模式识别得到的快速眼动，以及从整流和积分的颏下肌肌电图信号得到的肌张力。这些结果与脑电图、眼电图和颏下肌肌电图的原始信号一起在用户定义的时间尺度上显示。因此，睡眠阶段的视觉评分更容易。此外，一个包含提取参数的顺序和加权因子的决策表用于进行自动睡眠评分。
