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Electroencephalogram Signal Analysis in Alzheimer's Disease Early Detection

Author

Listed:
  • 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)

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%).

Suggested Citation

  • Pedro Miguel Rodrigues & Diamantino Rui Freitas & João Paulo Teixeira & Dílio Alves & Carolina Garrett, 2018. "Electroencephalogram Signal Analysis in Alzheimer's Disease Early Detection," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 7(1), pages 40-59, January.
    • Handle: RePEc:igg:jrqeh0:v:7:y:2018:i:1:p:40-59
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