Electroencephalogram Signal Analysis in Alzheimer's Disease Early Detection uri icon


  • 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%).
  • The authors gratefully acknowledge the opportunity to publish this work to the International Conference on Health and Social Care Information Systems and Technologies Co-chairs and to Neurological Unity of “Hospital de São João” for supplying the EEG signals used in this study.

publication date

  • January 1, 2018