Artificial Apnea Classification with Quantitative Sleep EEG Synchronization

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Aksahin M., Aydin S., Fırat H., Eroğul O.

JOURNAL OF MEDICAL SYSTEMS, cilt.36, ss.139-144, 2012 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1007/s10916-010-9453-8
  • Dergi Adı: JOURNAL OF MEDICAL SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.139-144
  • Anahtar Kelimeler: Sleep EEG, Apnea, EEG classification, Mutual information, Coherence function
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Hacettepe Üniversitesi Adresli: Hayır

Özet

https://link.springer.com/article/10.1007/s10916-010-9453-8

ABSTRACT

In the present study, both linear and nonlinear EEG synchronization methods so called Coherence Function (CF) and Mutual Information (MI) are performed to obtain high quality signal features in discriminating the Central Sleep Apnea (CSA) and Obstructive Sleep Apnea (OSA) from controls. For this purpose, sleep EEG series recorded from patients and healthy volunteers are classified by using several Feed Forward Neural Network (FFNN) architectures with respect to synchronic activities between C3 and C4 recordings. Among the sleep stages, stage2 is considered in tests. The NN approaches are trained with several numbers of neurons and hidden layers. The results show that the degree of central EEG synchronization during night sleep is closely related to sleep disorders like CSA and OSA. The MI and CF give us cooperatively meaningful information to support clinical findings. Those three groups determined with an expert physician can be classified by addressing two hidden layers with very low absolute error where the average area of CF curves ranged form 0 to 10 Hz and the average MI values are assigned as two features. In a future work, these two features can be combined to create an integrated single feature for error free apnea classification.

 

DISCUSSION AND ONCLUSION

The MI and CF have recently been applied to sleep EEGseries in discriminating apnea disorders so called CSA and OSA and apnea free healthy controls. To show the usefulness of EEG synchronization concept, the FFNN architectures have been successfully performed to classify these three groups where the average MI values and the average area of CF curves are assigned as signal features. The results support that, the degreeofEEGsynchronizationbetweenthecentral regions of right and left hemispheres are depend on brain dysfunctions closely related to apnea disorders. In conclusion, the CF and MI can cooperatively characterize apnea disorders. We can also conclude that these two synchronization measures can detect the difference of two synchronic sleep EEG series. In summary, we propose both linear and nonlinear EEG synchronization quantities for sleep EEG analysis in a large variety of sleep disorders from insomnia to apnea. In a future work, we will combine the average MI values and the average area of CF curves to obtain a single feature for classification of apnea with error free. Additionally, Global Field Synchronization [20] and Omega Complexity [21] can also be performed cooperatively to characterize the functional sleep quality and degree of EEG complexity by means of EEG synchronization in a future work.