JOURNAL OF MEDICAL SYSTEMS, cilt.39, 2015 (SCI-Expanded)
https://link.springer.com/article/10.1007%2Fs10916-015-0219-1
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.
Discussion and Conclusion: In the present study, four
hemispheric connectivity measurements were examined to obtain the
electrophysiological arousals on sleep EEG epoches recorded from healthy
controls and patients with PPI. All individuals can be classified correctly by
using any data mining classifier for both entropy based MI estimations and
spectral connectivity measurement so called coherence created by Welch’ method.
When the Burg method was performed to compute the power spectral density
estimation of sleep EEG epoch in estimating coherence, error free
classification can be obtained by using only three classifiers (RBFNetwork,
NNge, SMO). Regarding as PCC estimations, one person was misclassified in all
classifiers. Concerning phase coherence estimations, one or two individuals
were always misclassified.
In particular, lower
interhemispheric coherence and lower MI estimations as well as higher PCC
values were provided by patients in comparison to controls. In fact, only
linear relations between particular hemispheric locations could be observed by
using coherence, whereas the MI can measure both linear and nonlinear
statistical dependencies of hemispheres in time domain. The results support
that the cortex becomes more inactive as the sleep stage goes through from one
stage to the next one in non REM sleep periods (stage.1–4), however, the cortex
becomes much more active. It means that more neurons will be active in
processing the information transmission during REM sleep in REM sleep periods.
The higher order statistics of time series can be represented by nonlinear
approaches, regarding as the information theory [7].
Therefore, the MI provided the most useful estimations.
The MI can give information in
the context of functional connectivity such that its value highly depends on
the accuracy of estimated JE derived from probability distribution. The results
revealed that temporal dependency of cerebral hemispheres by means of MI can
provide a very efficient tool for detection of PPI from sleep EEG recordings.
The MI is a measure of statistical dependence between two random time series
without making any assumption on the nature of these signals. Since, the
duration of each single epoch was long enough, MI estimations gave stable
estimates. Another factor making the MI be successful in detecting hemispheric
functional changes between controls and PPI is that sleep EEG series are narrow
band signals as stated in reference [4].
In the further study, the
relationship between sleep stages and information transmission of multi-channel
EEG measurements in controls will be investigated. Additionally, MI will be
used to analyze sleep EEG series in detecting the effects of mood disorder
depending on functional disorganization of the brain.