Aplicação de redes neurais artificiais paraconsistentes como método de auxílio no diagnóstico da doença de Alzheimer

Abstract

The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient movement, electrical records, and physician interpretation of the visual analysis variation. The Artificial Neural Network (ANN) could be a helpful tool, appropriate to address problems such as prediction and pattern recognition. In this work, it has use a new class of ANN, the Paraconsistent Artificial Neural Network (PANN), which is capable of handling uncertain, inconsistent, and paracomplet information, for recognizing predetermined patterns of EEG and to assess its value as a possible auxiliary method for AD diagnosis. Thirty three patients with Alzheimer's disease and thirty four controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 Hz and 12.0 Hz (with an average frequency of 10 Hz), allowing a range of 0.5 Hz. The PANN was able to recognize waves that belonging to their respective bands of clinical use (theta, delta, alpha, and beta), leading to an agreement with the clinical diagnosis at 82% of sensitivity and at 61% of specificity. Supported with these results, the PANN could be a promising tool to manipulate EEG analysis, bearing in mind the following considerations: the growing interest of specialists in EEG analysis visual and the ability of the PANN to deal directly imprecise, inconsistent and paracomplet data, providing an interesting quantitative and qualitative analysis.

Paraconsistent neurocomputing and brain signal analysis

“…In the case of Alzheimer's disease, for example, the studies carried out previously have shown satisfactory results [20] (but still far from being a tool to aid clinical) that demonstrated the computational efficiency of the methodology using a simple morphological analysis (only paraconsistent annotated logic Eτ ). These results encouraged us to improve the morphological analysis of the waves and try to apply the method in other diseases besides Alzheimer's disease.…”

Paraconsistent artificial neural networks and EEG

“…In the case of Alzheimer's disease, for example, in studies carried out previously shown satisfactory results [10] (but still far from being a tool to aid clinical) that demonstrated the computational efficiency of the methodology using a simple morphological analysis (only paraconsistent annotated logic Eτ). These results encouraged us to improve the morphological analysis of the waves and try to apply the method in other diseases besides Alzheimer's disease.…”

Banco de dados inteligente e ferramentas associadas de sequências, mutações e resistências ao antiretrovirais do vírus HIV.

“…Neste modo de análise, todas as informações, tanto as contraditórias como a do terceiro especialista são relevantes e, portanto, consideradas no resultado.A primeira camada do sistema é composta por três células de conexão analítica: C1, C2 e C3 cujos sinais são analisados através da Equação Estrutural Básica (EEB) resultando nos sinais de saída Sa, Sb e SC. EBB para fazer a última análise entre os sinais apresentados nas saídas Sf e Sg (DA SILVA FILHO;LOPES, 2008) Adaptado de DA SILVA FILHO, J.I., ABE, J.M., 1999. e LOPES, H.F.S., 2008. Célula neural artificial paraconsistente de conexão lógica simples de maximização.No processo final da análise realizada pela RNAP, temos os valores resultantes de saída µr (valor de evidência favorável) e λr (valor de evidência contrária).…”