Automatic detection of sleep stages using the EEG
We present a method for the detection of sleep stages using the EEG (electroencephalogram). The method consists of four steps: segmentation; parameter extraction; cluster analysis; and classification. The parameters we compared were the parameters of Hjorth, the harmonic parameters and the relative band energy. For cluster analysis we used a modified version of the K-means algorithm. It is shown that the investigated parameters are capable of, extracting information from the EEG relevant for sleep stage scoring. Using the modified K-means algorithm it is possible to find 'similar' segments and hence automate the detection of sleep stages. However, extra information, e.g. the ECG (electrocardiogram) or the EOG (electrooculogram), is probably necessary for a clear discrimination between the different sleep stages.