Computer Based Synchronization Analysis on Sleep EEG in Insomnia

Creative Commons License

Aydin S.

JOURNAL OF MEDICAL SYSTEMS, vol.35, pp.517-520, 2011 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 35
  • Publication Date: 2011
  • Doi Number: 10.1007/s10916-009-9387-1
  • Journal Name: JOURNAL OF MEDICAL SYSTEMS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED)
  • Page Numbers: pp.517-520
  • Keywords: Sleep EEG, Coherence function, Mutual information, Insomnia, EIGENMODES, PARAMETERS
  • Hacettepe University Affiliated: No

Abstract

https://link.springer.com/article/10.1007%2Fs10916-009-9387-1

Inter-hemispheric sleep EEG coherence is studied in 10 subjects with psycho physiological insomnia, in 10 with paradoxical insomnia, and in 10 matched controls through different states of the sleep/wakefulness cycle. Inter hemispheric EEG coherence between central electrode pairs are compared to each other within these groups. A linear measure called as Coherence Function (CF) and a nonlinear measure called as Mutual Information (MI) are performed by using the Information Theory Toolbox in the present sleep EEG synchronization study. Regarding as tests, for all-night EEG recordings of participants, both measures indicate higher degree of EEG coherence for insomnia than for controls. The results further validate inter-hemispheric CF as a sign of activity in insomnia where the EEG series from stage2, REM sleep and the eyes closed waking state. In particular, the CF is found to be more useful tool than the MI for detection of insomnia when the power spectral density estimations of sleep stages are provided by the Burg Method.

In conclusion, the CF provides insights into functional connectivity of brain regions during sleep. Since the CF has a characteristic shape for sleep states, it can be proposed to identify the degree of EEG complexity depending on sleep disorders. In the present study, insomnia is analyzed in frequency domain by using both linear (i.e., CF) and nonlinear (i.e., MI) EEG synchronization measures. Both intervals of CF and MI results give that the higher degree of EEG synchronization is observed when the brain can not asleep well.

The CF is more useful tool than MI in sleep EEG analysis. Besides, it can be said that the clearest difference between ordered and disordered EEG series can be obtained via observation of frequency domain EEG synchronization for REM stages with respect to the other sleep stages.

In conclusion, the degree of EEG synchronization depends on healthy conditions in insomnia. Much lower CF values are obtained if there is no sleep disorder. Therefore, if people have a sleep disorder, it can be detected by consulting the CF curves of sleep EEG. In other words, if one has no sleep disorder, no high EEG synchronization is observed in association with any sleep stages. So, the CF can be proposed to observe the sleep EEG synchronization for detection of insomnia where the PSD estimations should be computed by using the BM. The basic idea of this proposition is that all the sleep stages are assumed to be modeled by low order AR model.

As further works, other synchronization measure based on multichannel applications such as Omega Complexity as a global synchronization [14] and phase synchronization [15] will be addressed for sleep EEG analysis in insomnia in future work.