Electroencephalogram Signal Analysis in Alzheimer's Disease Early Detection

Pedro Miguel Rodrigues, Diamantino Rui Freitas, João Paulo Teixeira, Dílio Alves and Carolina Garrett
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Pedro Miguel Rodrigues: Department of Electrical and Computer Engineering, University of Porto, Porto, Portugal
Diamantino Rui Freitas: University of Porto, Porto, Portugal
João Paulo Teixeira: Electrical Department, Polythecnic Institute of Bragança, Bragança, Portugal
Dílio Alves: Neurophysiology Department, Hospital de São João, Porto, Portugal
Carolina Garrett: Hospital de São João, Porto, Portugal

International Journal of Reliable and Quality E-Healthcare (IJRQEH), 2018, vol. 7, issue 1, 40-59

Abstract: The World's health systems are now facing a global problem known as Alzheimer's disease (AD) that mainly affects the elderly. The goal of this work is to perform a classification methodology skilled with Artificial Neural Networks (ANN) to improve the discrimination accuracy amongst patients at AD different stages comparatively to the state-of-art. For that, several study features that characterized the Electroencephalogram (EEG) signals “slow-down” were extracted and presented to the ANN entries in order to classify the dataset. The classification results achieved in the present work are promising concerning AD early diagnosis and they show that EEG can be a good tool for AD detection (Controls (C) vs AD: accuracy 95%; C vs Mild-cognitive Impairment (MCI): accuracy 77%; MCI vs AD: accuracy 83%; All vs All: accuracy 90%).

Date: 2018
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