Discrimination of Psychotic Symptoms from Controls Through Data Mining Methods Based on Emotional Principle Components

Maras A., AYDIN S.

International Conference on Medical and Biological Engineering in Bosnia and Herzegovina (CMBEBIH), Sarajevo, Bosna-Hersek, 16 - 18 Mart 2017, cilt.62, ss.26-30 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 62
  • Doi Numarası: 10.1007/978-981-10-4166-2_5
  • Basıldığı Şehir: Sarajevo
  • Basıldığı Ülke: Bosna-Hersek
  • Sayfa Sayıları: ss.26-30
  • İstanbul Üniversitesi Adresli: Evet

Özet

In this study, different data mining techniques has been used for classification of healthy controls and patients diagnosed by First Episode Psychosis with respect to complexity of frequency band activities (Delta, Theta, Alpha, Beta, Gamma)in multi channel EEG measurements mediated by emotional, static and visual stimuli including affective pictures from TAPS. Degree of local EEG complexity has been correlated by largeness of the dominant principle component in each EEG sub-band. The best classification performances are provided by Rotation Forest, Simple Logistic and Artificial Neural Networks when the components from occipito-parietal and posteriotemporal locations (P3, P4, O1, O2, T5 and T6) are considered as features in Gamma with respect to neutral emotional state.