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