Research Information System
JOURNAL OF MEDICAL SYSTEMS, vol.39, 2015 (SCI-Expanded)
https://link.springer.com/article/10.1007%2Fs10916-015-0219-1
ABSTRACT
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.