PubMed1987
For the classification of sleep stages, international standards based on visual EEG analysis have been established and are in common use, although we are well aware of their limitations. Several authors have suggested different procedures for classifying the stages of vigilance during the waking stages. No universally accepted paradigm, however, has yet been developed. The proposed vigilance classification procedures are based either on visual or automatic analysis procedures. Even though the EEG activity and patterns that reflect vigilance changes have been identified and described as indicators of the state of alertness, opinion is divided on how these should be combined in a vigilance classification rule. Automatic methods, on the other hand, have up to now used only part of the information available, the relationship of which to vigilance indicators has only been partially explored. The COMSTAT (Dept. of Computation and Statistics, AFB-Arzneimittelforschung, Berlin, FRG) rule combines visual and automatic analysis procedures. Different vigilance-dependent EEG patterns, such as the proportion of occipital background rhythm under resting conditions and its replacement by either faster or slower waves, the frequency range of the occipital rhythm and the anteriorization phenomena, have been used as information for a latent class analysis (LCA5) with 5 classes (stages of vigilance). There is a high correlation between the results of the LCA5 with visual classification rules made by experts. Using a robust discriminant analysis function which takes into account prior probabilities of the classes, and with a linear cost function for misclassification, an automatic rule with power spectrum variables was fitted to the results of the LCA5. Reclassification and split-half classification showed a high overlap between LCA5 and automatic classification. The result of this procedure is a new vigilance classification rule that is based on an objective mathematical rationale for the combination of different vigilance-indicative EEG activities and patterns but which can be applied to power-spectral estimators in an automatic EEG analysis procedure.