Mutual Information Analysis of Sleep EEG in Detecting Psycho-Physiological Insomnia


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Aydin S., Tunga M. A., Yetkin S.

JOURNAL OF MEDICAL SYSTEMS, cilt.39, 2015 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1007/s10916-015-0219-1
  • Dergi Adı: JOURNAL OF MEDICAL SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Anahtar Kelimeler: Sleep EEG, Brain connectivity, Mutual information, Data mining, Classification, FUNCTIONAL CONNECTIVITY, COHERENCE, TRANSMISSION, SIGNALS
  • Hacettepe Üniversitesi Adresli: Hayır

Özet

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.