Helder Frederico da Silva Lopes

Index of Alpha/Theta Ratio of the Electroencephalogram: A New Marker for Alzheimer’s Disease

Objective: We evaluated quantitative EEG measures to determine a screening index to discriminate Alzheimer’s disease (AD) patients from normal individuals. Methods: Two groups of individuals older than 50 years, comprising a control group of 57 normal volunteers and a study group of 50 patients with probable AD, were compared. EEG recordings were obtained from subjects in a wake state with eyes closed at rest for 30 min. Logistic regression analysis was conducted. Results: Spectral potentials of the alpha and theta bands were computed for all electrodes and the alpha/theta ratio calculated. Logistic regression of alpha/theta of the mean potential of the C3 and O1 electrodes was carried out. A formula was calculated to aid the diagnosis of AD yielding 76.4% sensitivity and 84.6% specificity for AD with an area under the ROC curve of 0.92. Conclusion: Logistic regression of alpha/theta of the spectrum of the mean potential of EEG represents a good marker discriminating AD patients from normal controls.

Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study

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.

Alzheimer's disease qEEG: spectral analysis versus coherence. which is the best measurement?

There is evidence in electroencephalography that alpha, theta and delta band oscillations reflect cognitive and memory performances and that quantitative techniques can improve the electroencephalogram (EEG) sensitivity. This paper presents the results of comparative analysis of qEEG variables as reliable markers for Alzheimers disease (AD). We compared the sensitivity and specificity between spectral analysis (spectA) and coherence (Coh) within the same group of AD patients. SpectA and Coh were calculated from EEGs of 40 patients with mild to moderate AD and 40 healthy elderly controls. The peak of spectA was smaller in the AD group than in controls. AD group showed predominance of slow spectA in theta and delta bands and a significant reduction of inter-hemispheric Coh for occipital alpha 2 and beta 1 and for frontal delta sub-band. ROC curve supported that alpha band spectA was more sensitive than coherence to differentiate controls from AD.

Paraconsistent Artificial Neural Networks and Delta, Theta, Alpha, and Beta Bands Detection

In this work we present a study of brain EEG waves – delta, theta, alpha, and gamma bands employing a new ANN based on Paraconsistent Annotated Evidential Logic E( which is capable of manipulating concepts like impreciseness, inconsistency, and paracompleteness in a nontrivial manner. We present the Paraconsistent Artificial Neural Network – PANN with some detail and discuss some applications.

Paraconsistents artificial neural networks applied to the study of mutational patterns of the F subtype of the viral strains of HIV-1 to antiretroviral therapy

The high variability of HIV-1 as well as the lack of efficient repair mechanisms during the stages of viral replication, contribute to the rapid emergence of HIV-1 strains resistant to antiretroviral drugs. The selective pressure exerted by the drug leads to fixation of mutations capable of imparting varying degrees of resistance. The presence of these mutations is one of the most important factors in the failure of therapeutic response to medications. Thus, it is of critical to understand the resistance patterns and mechanisms associated with them, allowing the choice of an appropriate therapeutic scheme, which considers the frequency, and other characteristics of mutations. Utilizing Paraconsistents Artificial Neural Networks, seated in Paraconsistent Annotated Logic Et which has the capability of measuring uncertainties and inconsistencies, we have achieved levels of agreement above 90% when compared to the methodology proposed with the current methodology used to classify HIV-1 subtypes. The results demonstrate that Paraconsistents Artificial Neural Networks can serve as a promising tool of analysis.

Paraconsistent Neurocomputing and Biological Signals Analysis

In this work, we show two applications of Paraconsistent Artificial Neural Network (PANN) for signal analysis working with signal data as a numeric vector and analyzing its morphology, comparing the signal data with a reference database and their application as support for electroencephalogram exams and HIV genotyping.

Some recent results in paraconsistent artificial neural networks

In this work, we present a short overview of some studies made with paraconsistent artificial neural network (PANN). PANN is a new type of artificial neural network (ANN) based on a new class of paraconsistent logics, namely paraconsistent annotated evidential logic Eτ, which is capable of manipulating concepts like impreciseness, inconsistency, and paracompleteness in a non–trivial manner.

Paraconsistent artificial neural networks and AD analysis: improvements

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.

Applications of paraconsistent artificial neural networks in EEG

In this work we summarize all our studies on Paraconsistent Artificial Neural Networks applied to electroencephalography.

Paraconsistent artificial neural networks and EEG analysis

The aim of this paper is to present a study of brain EEG waves through a new ANN based on Paraconsistent Annotated Evidential Logic Eτ which is capable of manipulating concepts like impreciseness, inconsistency, and paracompleteness in a nontrivial manner. As application, the Paraconsistent Artificial Neural Network - PANN showed capable of recognizing children with Dyslexia with Kappa index at a rate of 80%.