Mutual information analysis of sleep EEG in detecting psycho-physiological insomnia.

The primary goal of this study is to state the clear changes in functional brain connectivity during all night sleep in psycho-physiological insomnia (PPI). The secondary goal is to investigate the usefulness of Mutual Information (MI) analysis in estimating cortical sleep EEG arousals for detection of PPI. For these purposes, healthy controls and patients were compared to each other with respect to both linear (Pearson correlation coefficient and coherence) and nonlinear quantifiers (MI) in addition to phase locking quantification for six sleep stages (stage.1-4, rem, wake) by means of interhemispheric dependency between two central sleep EEG derivations. In test, each connectivity estimation calculated for each couple of epoches (C3-A2 and C4-A1) was identified by the vector norm of estimation. Then, patients and controls were classified by using 10 different types of data mining classifiers for five error criteria such as accuracy, root mean squared error, sensitivity, specificity and precision. High performance in a classification through a measure will validate high contribution of that measure to detecting PPI. The MI was found to be the best method in detecting PPI. In particular, the patients had lower MI, higher PCC for all sleep stages. In other words, the lower sleep EEG synchronization suffering from PPI was observed. These results probably stand for the loss of neurons that then contribute to less complex dynamical processing within the neural networks in sleep disorders an the functional central brain connectivity is nonlinear during night sleep. In conclusion, the level of cortical hemispheric connectivity is strongly associated with sleep disorder. Thus, cortical communication quantified in all existence sleep stages might be a potential marker for sleep disorder induced by PPI.