Paraconsistent neurocomputing and brain signal analysis

Abstract

In this work we summarize some of our studies on paraconsistent artificial neural networks (PANN) applied to electroencephalography. We give attention to the following applications: probable diagnosis of Alzheimer disease and attention-deficit /hyperactivity disorder (ADHD). PANNs are well suited to tackle problems that human beings are good at solving, like prediction and pattern recognition. PANNs have been applied within several branches and among them, the medical domain for clinical diagnosis, image analysis, and interpretation signal analysis, and interpretation, and drug development. For study of ADHD, we have a result of recognition electroencephalogram standards (delta, theta, alpha, and beta waves) with a median kappa index of 80 %. For study of the Alzheimer disease, we have a result of clinical diagnosis possible with 80 % of sensitivity, 73 % of specificity, and a kappa index of 76 %.