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Electroencephalogram analysis with approximate entropy to help in the diagnosis of Alzheimer's disease

Abásolo, D, Hornero, R, Espino, P, Alonso, A and de la Rosa, R (2003) Electroencephalogram analysis with approximate entropy to help in the diagnosis of Alzheimer's disease In: 4th International Conference on Information Technology Applications in Biomedicine (ITAB 2003), 2003-04-24 - 2003-04-26, BIRMINGHAM, ENGLAND.

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Abstract

Alzheimer's disease (AD) is the main cause of dementia in western countries. Although a definite diagnosis of this illness is only possible by necropsy, the analysis of nonlinear dynamics in electroencephalogram (EEG) signals could help physicians in this difficult task In this study we have applied Approximate Entropy (ApEn) to analyze the EEG background activity of patients with a clinical diagnosis of Alzheimer's disease and control subjects. ApEn is a newly introduced statistic that can be used to quantify the complexity (or irregularity) of a time series. We have divided the EEG data into frames to calculate their ApEn. Our results show that the degree of complexity of EEGs from control subjects is higher. Applying the ANOVA test, we have verified that there was a significant difference (p < 0.05) between the EEGs of these groups.

Item Type: Conference or Workshop Item (Paper)
Additional Information:

Copyright 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Uncontrolled Keywords: Alzheimer's disease (AD), electroencephalogram (EEG), approximate entropy (ApEn), complexity, COMPLEXITY, REGULARITY, ATTRACTORS, DYNAMICS, HORMONE
Divisions: Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Depositing User: Symplectic Elements
Date Deposited: 27 Sep 2012 10:48
Last Modified: 23 Sep 2013 19:36
URI: http://epubs.surrey.ac.uk/id/eprint/713542

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