loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Sérgio Daniel Rodrigues 1 ; João Paulo Teixeira 1 and Pedro Miguel Rodrigues 2

Affiliations: 1 Polytechnic Institute of Bragança, Portugal ; 2 University of Porto, Portugal

ISBN: 978-989-8565-36-5

Keyword(s): Electroencephalogram, Alzheimer’s Disease, Artificial Neural Network.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Detection and Identification ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Neurodegenerative disorders associated with aging as Alzheimer’s disease (AD) have been increasing significantly in the last decades. AD affects the cerebral cortex and causes specific changes in brain electrical activity. Therefore, the analysis of signals from the electroencephalogram (EEG) may reveal structural and functional deficiencies typically associated with AD. This study aimed to develop an Artificial Neural Network (ANN) to classify EEG signals between cognitively normal control subjects and patients with probable AD . The results showed that the EEG can be a very useful tool to obtain an accurate diagnosis of AD. The best results were performed using the Power Spectral Density (PSD) determined by Short Time Fourier Transform (STFT) with a ANN developed using Levenberg - Marquardt training algorithm, Logarithmic Sigmoid activation function and 9 nodes in the hidden layer (correlation coefficient training: 0.99964, test: 0.95758 and validation: 0.9653 and with a total of: 0 .99245). (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.161.118.57

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Daniel Rodrigues, S.; Daniel Rodrigues, S.; Paulo Teixeira, J. and Miguel Rodrigues, P. (2013). EEG Discrimination with Artificial Neural Networks.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 236-241. DOI: 10.5220/0004249702360241

@conference{biosignals13,
author={Sérgio Daniel Rodrigues. and Sérgio Daniel Rodrigues. and João Paulo Teixeira. and Pedro Miguel Rodrigues.},
title={EEG Discrimination with Artificial Neural Networks},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={236-241},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004249702360241},
isbn={978-989-8565-36-5},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - EEG Discrimination with Artificial Neural Networks
SN - 978-989-8565-36-5
AU - Daniel Rodrigues, S.
AU - Daniel Rodrigues, S.
AU - Paulo Teixeira, J.
AU - Miguel Rodrigues, P.
PY - 2013
SP - 236
EP - 241
DO - 10.5220/0004249702360241

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.