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Singular Spectrum Analysis of Sleep EEG in Insomnia

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Abstract

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.

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Acknowledgements

The authors would like to thank Prof. Dr. Fuat ÖZGEN of the Psychiatry Department, who have performed the recordings at the Gülhane Military Medical Academia.

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Authors and Affiliations

  1. Engineering Faculty, Electrical and Electronics Eng. Department, Ondokuz Mayıs University, Samsun, Turkey

    Serap Aydın

  2. Engineering Faculty, Electrical and Electronics Eng. Department, Dumlupınar University, Kütahya, Turkey

    Hamdi Melih Saraoǧlu

  3. Biomedical Engineering Institute, Fatih University, Istanbul, Turkey

    Sadık Kara

Authors
  1. Serap Aydın
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  2. Hamdi Melih Saraoǧlu
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  3. Sadık Kara
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Correspondence to Serap Aydın.

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Aydın, S., Saraoǧlu, H.M. & Kara, S. Singular Spectrum Analysis of Sleep EEG in Insomnia. J Med Syst 35, 457–461 (2011). https://doi.org/10.1007/s10916-009-9381-7

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  • DOI: https://doi.org/10.1007/s10916-009-9381-7

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