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Advisor(s)
Abstract(s)
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%).
Description
Keywords
Alzheimer’s disease Artificial neural networks Classification Early diagnosis Electroencephalogram signals Features, stages
Pedagogical Context
Citation
Rodrigues, Pedro Miguel; Freitas, Diamantino Rui; Teixeira, João Paulo; Alves, Dílio; Garrett, Carolina (2018). Electroencephalogram signal analysis in Alzheimer's disease early detection. International Journal of Reliable and Quality E-Healthcare. ISSN 2160-9551. 7:1, p. 40-59
Publisher
IGI Global