Automatic recognition of Alzheimer's disease using genetic algorithms and neural network

  • Authors:

  • Sunyoung Cho;Boyeon Kim;Eunhea Park;Yunseok Chang;Jongwoo Kim;Kyungchun Chung;Weiwan Whang;Hyuntaek Kim

  • Affiliations:

  • Basic Science Research Institute, Chungbuk National University, Chungju, Korea;Department of Electrical & Computer Engineering, Kangwon National University, Chuncheon, Korea;Department of Psychology, Korea University, Seoul, Korea;Department of Computer Engineering, Daejin University, Pocheon, Korea;Department of Oriental Neuropsychiatry, Kyunghee University, Seoul, Korea;Department of Neurology, Kyunghee University, Seoul, Korea;Department of Oriental Neuropsychiatry, Kyunghee University, Seoul, Korea;Department of Psychology, Korea University, Seoul, Korea

  • Venue:
  • ICCS'03 Proceedings of the 2003 international conference on Computational science: PartII
  • Year:
  • 2003

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Abstract

We propose an Alzheimer's disease (AD) recognition method combined the genetic algorithms (GA) and the artificial neural network (ANN). Spontaneous EEG and auditory ERP data recorded from a single site in 16 early AD patients and 16 age-matched normal subjects were used. We made a feature pool including 88 spectral, 28 statistical and 2 nonlinear characteristics of EEG and 10 features of ERP. The combined GA/ANN was applied to find the dominant features automatically from the feature pool, and the selected features were used as a network input. The recognition rate of the ANN fed by this input was 81.9% for the untrained data set. These results lead to the conclusion that the combined GA/ANN approach may be useful for an early detection of the AD. This approach could be extended to a reliable classification system using EEG recording that can discriminate between groups.