Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study

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.ObjectivesTo employ the Paraconsistent Artificial Neural Network to ascertain how to determine the degree of certainty of probable dementia diagnosis.MethodsTen 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.ResultsThe 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.

Analysis of long range dependence in the EEG signals of Alzheimer patients

“…A decrease in the relative logarithmic transformed power spectral density has been reported in the right temporal of AD patients (Aghajani et al 2013). The paraconsistent artificial neural network (PANN) was capable of accurately recognizing slowing of alpha rhythm in Alzheimer patient (Abe et al 2007).…”

Improving Alzheimer's Disease Diagnosis with Machine Learning Techniques

“…This technique is known by its good generalization ability and robustness to process high dimensional data as EEG signals. 26 –29…”

Introduction to Annotated Logics

“…The more such constructs exist, the better. So in most cases, it is convenient to suppose that U is a universe, i.e., a model of Z F. For example, we have: 12 If U is a universe, λ ∈ τ , and X ∈ U , then X [λ,e] ∈ U and X (λ,e) ∈ U and F X ∈ U . Furthermore,…”

“…PANN is useful to the problems in data analysis, expert system, speech recognition, etc. Abe et al [12] used PANNs to deal with Alzheimer's disease.…”