On the stationarity and normality of the electroencephalographic data during sleep stages.
In this paper, we show techniques to examine the stationarity and the normality of time series as well as results obtained by applying these techniques to EEG data during sleep stages. Many statistical analyses of the EEG data are based on the assumption that the EEG data are stationary and normally distributed. However, the problem is to know the length of data which is most appropriate for the statistical analysis. From the analysis, it is found that a decreased length of data implies an increased degree of stationarity and normality in each stage of sleep. However, for the practical stationary analysis of EEG data, it is shown that it is appropriate to take about 5 s during sleep state. Stationarity is fulfilled to a relatively large extent in REM but more seldom in stage 2. In the case of short data, asymmetric distribution occupies the large parts of non-normal distribution. As the length of data increases the proportion which is symmetric but sharper or flatter than the normal distribution increases.
D003201:Computers; D004569:Electroencephalography; D008954:Models, Biological; D012894:Sleep Stages; D013223:Statistics as Topic