  Sleep stage classification is a widely discussed topic, due to its importance
in the diagnosis of sleep disorders, e.g. insomnia. Analysis of the brain
activity during sleep is necessary to gain further insight into the processing
that occurs in our brains. We want to use permutation entropy as a model for
this analysis. Therefore, the signal processing in terms of
electroencephalography is described. This results in a time discrete signal,
that can be further processed by applying the method of permutation entropy,
which is a modification of the Shannon entropy as a measure of information
processing. The method is applied to 18 data sets, nine electroencephalography
measurements of patients suffering from insomnia and nine of people without a
sleep disorder. A strong correlation between the permutation entropy value and
the sleep stages was found during the simulation runs. The results are analysed
and presented using boxplot diagrams of the permutation entropy over the sleep
stages. Furthermore, it is investigated that there is a steady decrease in the
value when the patient is in a deeper sleep. This suggests that the method is a
good parameter for sleep stage classification. Finally, we propose an extension
of the conceptual model to other pathological conditions and also to the
analysis of brain activity during surgery.
