Study of the MEG background activity in Alzheimer's disease patients with scaling analysis methods.
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Gómez, C, Hornero, R, Abásolo, D, Fernández, A and Poza, J (2009) Study of the MEG background activity in Alzheimer's disease patients with scaling analysis methods. In: Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, 2009-09-03 - 2009-09-06, United States.
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Official URL: http://dx.doi.org/10.1109/IEMBS.2009.5334569
Abstract
Alzheimer's disease (AD) is one of the most prominent neurodegenerative disorders. The aim of this research work is to study the magnetoencephalogram (MEG) background activity in AD patients using two scaling analysis methods: detrended fluctuation analysis (DFA) and backward detrended moving average (BDMA). Both measures have been designed to quantify correlations in noisy and non-stationary signals. Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 15 patients with probable AD and 15 control subjects. Both DFA and BDMA exhibited two scaling regions with different slopes. Significant differences between both groups were found in the second region of DFA and in the first region of BDMA (p < 0.01, Student's t-test). Using receiver operating characteristic curves, accuracies of 83.33% with DFA and of 80% with BDMA were reached. Our findings show the usefulness of these scaling analysis methods to increase our insight into AD.
| Item Type: | Conference or Workshop Item (Paper) |
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| Additional Information: | Copyright 2009 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, Aged, 80 and over, Algorithms, Alzheimer Disease, Artificial Intelligence, Female, Humans, Magnetoencephalography, Male, Middle Aged, Models, Statistical, Neural Networks (Computer), Pattern Recognition, Automated, ROC Curve, Reproducibility of Results, Signal Processing, Computer-Assisted |
| Divisions: | Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences |
| ID Code: | 713549 |
| Deposited By: | Symplectic Elements |
| Deposited On: | 02 Oct 2012 16:45 |
| Last Modified: | 11 May 2013 14:40 |
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