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Artificial Neural Networks in the Discrimination of Alzheimer’s Disease

  • Pedro Rodrigues
  • João Paulo Teixeira
Part of the Communications in Computer and Information Science book series (CCIS, volume 221)

Abstract

Alzheimer’s disease (AD) is the most common cause of dementia, a general term for memory loss and other intellectual abilities. The Electroencephalogram (EEG) has been used as diagnosis tool for dementia over several decades. The main objective of this work was to develop an Artificial Neural Network (ANN) to classify EEG signals between AD patients and Control subjects. For this purpose two different methodologies and variations were used. The Short time Fourier transform (STFT) was applied to one of the methodologies and the Wavelet Transform (WT) was applied to the other methodology. The studied features of the EEG signals were the Relative Power in conventional EEG bands (delta, theta, alpha, beta and gamma) and their associated Spectral Ratios (r 1, r 2, r 3 and r 4). The best classification was performed by the ANN using the WT Biorthogonal 3.5 with AROC of 0.97, Sensitivity of 92.1%, Specificity of 90.8% and 91.5% of Accuracy.

Keywords

Alzheimer’s Disease Electroencephalogram Artificial Neural Networks Short Time Fourier Transform Wavelet Transform 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pedro Rodrigues
    • 1
  • João Paulo Teixeira
    • 1
  1. 1.Polytechnic Institute of BragançaPortugal

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