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Dementia & Neuropsychologia

Print version ISSN 1980-5764

Dement. neuropsychol. vol.1 no.3 São Paulo July/Sept. 2007

http://dx.doi.org/10.1590/S1980-57642008DN10300004 

Original Articles

Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study

Rede neural artificial paraconsistente e doença de Alzheimer: estudo preliminar

Jair Minoro Abe 1  

Helder Frederico da Silva Lopes 2  

Renato Anghinah 3  

1Institute For Advanced Studies - University of São Paulo, Brazil.

2Graduate student of Medical School of University of São Paulo - Brazil.

3Reference Center of Behavioral Disturbances and Dementia (CEREDIC) of the Medical School of University of São Paulo, Brazil.


Abstract

EEG visual analysis has proved useful in aiding AD diagnosis, being indicated in some clinical protocols. However, such analysis is subject to the inherent imprecision of equipment, patient movements, electric registers, and individual variability of physician visual analysis. Objectives: To employ the Paraconsistent Artificial Neural Network to ascertain how to determine the degree of certainty of probable dementia diagnosis. Methods: Ten EEG records from patients with probable Alzheimer disease and ten controls were obtained during the awake state at rest. An EEG background between 8 Hz and 12 Hz was considered the normal pattern for patients, allowing a variance of 0.5 Hz. Results: The PANN was capable of accurately recognizing waves belonging to Alpha band with favorable evidence of 0.30 and contrary evidence of 0.19, while for waves not belonging to the Alpha pattern, an average favorable evidence of 0.19 and contrary evidence of 0.32 was obtained, indicating that PANN was efficient in recognizing Alpha waves in 80% of the cases evaluated in this study. Artificial Neural Networks - ANN - are well suited to tackle problems such as prediction and pattern recognition. The aim of this work was to recognize predetermined EEG patterns by using a new class of ANN, namely the Paraconsistent Artificial Neural Network - PANN, which is capable of handling uncertain, inconsistent and paracomplete information. An architecture is presented to serve as an auxiliary method in diagnosing Alzheimer disease. Conclusions: We believe the results show PANN to be a promising tool to handle EEG analysis, bearing in mind two considerations: the growing interest of experts in visual analysis of EEG, and the ability of PANN to deal directly with imprecise, inconsistent, and paracomplete data, thereby providing a valuable quantitative analysis.

Key words: EEG; Alzheimer disease; pattern recognition; artificial neural network; paraconsistent logic.

Resumo

A análise visual de EEG tem se mostrado útil na ajuda de diagnóstico de DA, sendo indicado em alguns protocolos clínicos. Porém, tal análise está sujeita à imprecisão inerente de equipamentos, movimentos do paciente, registros elétricos e variação individual da análise visual do médico. Objetivos: Utilizar a Rede Neural Artificial Paraconsistente para saber como determinar um grau de certeza no diagnóstico da doença de Alzheimer provável. Métodos: Dez pacientes com doença de Alzheimer provável e 10 controles foram submetidos ao registro de exames de EEG durante a vigília em repouso. Considerou-se como padrão normal de um paciente, a atividade de base entre 8,0 Hz a 12,0 Hz, permitindo uma variação de 0.5 Hz. Resultados: A RNAP foi capaz de reconhecer ondas que pertencem à banda Alfa como banda Alfa com evidência favorável de 0.30 e evidência contrária de 0.19, enquanto ondas não pertencentes ao padrão Alfa, foi obtido uma evidência favorável média de 0.19 e evidência contrária de 0.32, mostrando que a RNAP foi eficiente para reconhecer ondas Alfa, o que leva a uma concordância com o diagnóstico clínico de 80%. Conclusões: RNAP pode ser ferramenta promissora para manipular análise de EEG, tendo em mente ambas considerações: o interesse crescente de especialistas em análise visual de EEG e a capacidade da RNAP tratar diretamente dados imprecisos, inconsistentes e paracompletos, fornecendo uma interessante análise quantitativa.

Palavras-chave: EEG; doença de Alzheimer; rede neural artificial; reconhecimento de padrões; lógica paraconsistente.

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Jair Minoro Abe - Av. Prof. Luciano Gualberto / Travessa J, 374 / Térreo / Cidade Universitária - 05508-900 São Paulo SP - Brazil.

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