Singular Spectrum Analysis of Sleep EEG in Insomnia

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Aydin S., Saraoglu H. M., Kara S.

JOURNAL OF MEDICAL SYSTEMS, cilt.35, ss.457-461, 2011 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1007/s10916-009-9381-7
  • Dergi Adı: JOURNAL OF MEDICAL SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.457-461
  • Anahtar Kelimeler: Sleep EEG, Singular Spectrum Analysis, EEG classification
  • Hacettepe Üniversitesi Adresli: Hayır

Özet

https://link.springer.com/article/10.1007%2Fs10916-009-9381-7

In the present study, the Singular Spectrum Analysis (SSA) is applied to sleep EEG segments collected from healthy volunteers and patients diagnosed by either psycho physiological insomnia or paradoxical insomnia. Then, the resulting singular spectra computed for both C3 and C4 recordings are assigned as the features to the Artificial Neural Network (ANN) architectures for EEG classification in diagnose. In tests, singular spectrum of particular sleep stages such as awake, REM, stage1 and stage2, are considered. Three clinical groups are successfully classified by using one hidden layer ANN architecture with respect to their singular spectra. The results show that the SSA can be applied to sleep EEG series to support the clinical findings in insomnia if ten trials are available for the specific sleep stages. In conclusion, the SSA can detect the oscillatory variations on sleep EEG. Therefore, different sleep stages meet different singular spectra. In addition, different healthy conditions generate different singular spectra for each sleep stage. In summary, the SSA can be proposed for EEG discrimination to support the clinical findings for psycho-psychological disorders.

Discussion and conclusion

The SSA is newly applied to sleep EEG series to discriminate the psycho-psychological and paradoxical insomnia. Moreover, ANN architectures are performed for EEG classification to support the usefulness of the SSA for sleep EEG analysis.

In tests, the best embedding dimension is found to be 10 for the SSA in case of long records. Both specified two patient groups and controls provide the different singular spectra depending on the sleep stages. Considering the recording sites, we can said that the segments collected by C3 electrode are more suitable for sleep EEG analysis. In accordance with C3 records, classification accuracy of about 98% can be obtained when the singular spectra of the specified two subcategories of insomnia are considered as the signal features.

In conclusion, the singular spectra of sleep EEG series can characterize the insomnia. It can be said that, the SSA can detect the trend components of sleep EEG. Then, we propose the SSA in sleep EEG analysis for psycho-psychological disorders such as hypersomnia, schizophrenia, depression, etc.