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Electroencephalogram background activity characterization with approximate entropy and auto mutual information in Alzheimer's disease patients.

Abásolo, D, Hornero, R, Espino, P, Escudero, J and Gómez, C (2007) Electroencephalogram background activity characterization with approximate entropy and auto mutual information in Alzheimer's disease patients. In: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, 2007-08-22 - 2007-08-26, United States.

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Abstract

The aim of this study was to analyze the electroencephalogram (EEG) background activity in Alzheimer's disease (AD) with two non-linear methods: Approximate Entropy (ApEn) and Auto Mutual Information (AMI). ApEn quantifies the regularity in data, while AMI detects linear and non-linear dependencies in time series. EEGs were recorded from the 19 scalp loci of the international 10-20 system in 11 AD patients and 11 age-matched controls. ApEn was significantly lower in AD patients at electrodes O1, O2, P3 and P4 (p<0.01). The AMI of the AD patients decreased significantly more slowly with time delays than the AMI of normal controls at electrodes T5, T6, O1, O2, P3 and P4 (p<0.01). Furthermore, we observed a strong correlation between the results obtained with both non-linear methods, suggesting that the AMI rate of decrease can be used to estimate the regularity in time series. The decreased irregularity found in AD patients suggests that EEG analysis with ApEn and AMI could help to increase our insight into brain dysfunction in AD.

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

Copyright 2007 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: Aged, Algorithms, Alzheimer Disease, Artificial Intelligence, Brain, Diagnosis, Computer-Assisted, Entropy, Female, Humans, Magnetoencephalography, Male, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity
Divisions: Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Depositing User: Symplectic Elements
Date Deposited: 02 Oct 2012 13:54
Last Modified: 23 Sep 2013 19:36
URI: http://epubs.surrey.ac.uk/id/eprint/713543

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