Computerized recognition of Alzheimer disease-EEG using genetic algorithms and neural network

  • Authors:

  • Hyun Taek Kim;Bo Yeon Kim;Eun Hye Park;Jong Woo Kim;Eui Whan Hwang;Seung Kee Han;Sunyoung Cho

  • Affiliations:

  • Department of Psychology, Korea University, Seoul, Republic of Korea;Department of Electrical and Computer Engineering, Kangwon National University, Chuncheon, Republic of Korea;Department of Psychology, Korea University, Seoul, Republic of Korea;Department of Oriental Neuropsychiatry, Kyunghee University, Seoul, Republic of Korea;Department of Oriental Neuropsychiatry, Kyunghee University, Seoul, Republic of Korea;Basic Science Research Institute, Chungbuk National University, Cheongju, Republic of Korea;Basic Science Research Institute, Chungbuk National University, Cheongju, Republic of Korea

  • Venue:
  • Future Generation Computer Systems
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

We propose an automatic recognition method of Alzheimer's disease (AD) with single channel EEG recording using combined the genetic algorithms (GA) and the artificial neural network (ANN). Five min of the resting spontaneous EEG and the ERP in an auditory oddball task were recorded at P4 site in 16 early AD patients and 16 age-matched normal subjects. EEG and ERP were analyzed to compute their 28 statistical and 2 nonlinear features as well as 88 spectral features and 10 ERP features, to make a feature pool for each 30-s segment of the recording data. The combined GA/ANN was applied to find the minimal set of the dominant features from the feature pool that are most efficient to classify into two groups automatically. The effective 35 features were found and used as inputs of the artificial neural network. The recognition rate of ANN fed by these input was 81.9% for untrained data set. These results suggest that the combined GA/ANN approach may be useful for early detection of AD and that single channel EEG data might be enough to recognize AD. This approach could be extended to a reliable classification system using EEG recording that can discriminate between groups.