Recognition of Alzheimer's disease using quantitative electroencephalography



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Texas Tech University


Currently, Alzheimer's disease is diagnosed through a lengthy process, including patient history, neuropsychological testing, neurophysiological analysis, and psychological evaluations. There is hope that a quantitative, objective diagnostic procedure would increase diagnosis capabilities, including earlier detection, an increase in ease of diagnosis, and greater diagnosis consistency.

This thesis investigates using power values and complexity measures of the electroencephalogram as input features to a neural network for classification of Alzheimer's disease patients, mild cognitively impaired patients, and control subjects. Specifically, the complexity measures activity, complexity, and mobility, as well as the relative power values in frequency bands delta, theta, alpha, beta, and gamma are calculated for the electroencephalogram of each subject. Finally, a Learning Vector Quantization Neural Network will be designed to classify each subject into their respective category based upon an input vector consisting of these power features. The ability of this network to classify patients correctly will be measured and reported.



Alzheimer's disease -- Diagnosis, Neural networks (Computer science), Electroencephalography -- Evaluation