Abstract
This work is a sequel of our study of Alzheimer Disease – AD auxiliary diagnosis through EEG findings, with the aid of Paraconsistent Artificial Neural Network – PANN [3], [6], [7] through testing a new architecture of PANN whose expert systems are based on the profile of the EEG examination. This profile consists of the quantification of the waves grouped in clinically normal frequency bands (delta, theta, alpha and beta) plus the relationship alpha / theta.
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Abe, J.M., Lopes, H.F.S., Nakamatsu, K. (2012). Paraconsistent Artificial Neural Networks and AD Analysis – Improvements. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_27
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DOI: https://doi.org/10.1007/978-3-642-34630-9_27
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