EEG Classification for depression patients using decision tree and possibilistic support vector machines

뇌파의 의사 결정 트리 분석과 가능성 기반 서포트 벡터 머신 분석을 통한 우울증 환자의 분류

  • 심우현 (한국과학기술원 바이오시스템학과) ;
  • 이기영 (한국과학기술원 바이오시스템학과) ;
  • 채정호 (가톨릭 의과대학교 신경정신과) ;
  • 정재승 (한국과학기술원 바이오시스템학과) ;
  • 이도헌 (한국과학기술원 바이오시스템학과)
  • Published : 2006.05.28


Depression is the most common and widespread mood disorder. About 20% of the population might suffer a major, incapacitating episode of depression during their lifetime. This disorder can be classified into two types: major depressive disorders and bipolar disorder. Since pharmaceutical treatments are different according to types of depression disorders, correct and fast classification is quite critical for depression patients. Yet, classical statistical method, such as minnesota multiphasic personality inventory (MMPI), have some difficulties in applying to depression patients, because the patients suffer from concentration. We used electroencephalogram (EEG) analysis method fer classification of depression. We extracted nonlinearity of information flows between channels and estimated approximate entropy (ApEn) for the EEG at each channel. Using these attributes, we applied two types of data mining classification methods: decision tree and possibilistic support vector machines (PSVM). We found that decision tree showed 85.19% accuracy and PSVM exhibited 77.78% accuracy for classification of depression, 30 patients with major depressive disorder and 24 patients having bipolar disorder.