ABSTRACT
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.
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 paracomplete 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 34 controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 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 paracomplete data, providing an interesting quantitative and qualitative analysis.
