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.
This work was supported by Korean Ministry of Health and Welfare, 00-PJ9-PG1-CO05-0002 and a result of research activities of Advanced Biometric Research Center (ABRC) supported by KOSEF.
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Cho, S. et al. (2003). Automatic Recognition of Alzheimer’s Disease Using Genetic Algorithms and Neural Network. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J.J., Zomaya, A.Y. (eds) Computational Science — ICCS 2003. ICCS 2003. Lecture Notes in Computer Science, vol 2658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44862-4_75
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