Complexity analysis of the magnetoencephalogram background activity in Alzheimer's disease patients.
Gómez, C, Hornero, R, Abásolo, D, Fernández, A and López, M (2006) Complexity analysis of the magnetoencephalogram background activity in Alzheimer's disease patients. Med Eng Phys, 28 (9). 851 - 859. ISSN 1350-4533
Gomez_et_al_MedEngPhys_final_version_2006.pdf - Accepted Version
Available under License : See the attached licence file.
The aim of the present study was to analyse the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD) using the Lempel-Ziv (LZ) complexity. This non-linear method measures the complexity of finite sequences and is related to the number of distinct substrings and the rate of their occurrence along the sequence. The MEGs were recorded with a 148-channel whole-head magnetometer (MAGNES 2500 WH, 4D Neuroimaging) in 21 patients with AD and in 21 age-matched control subjects. Artefact-free epochs were selected for complexity analysis. Results showed that MEG signals from AD patients had lower complexity than control subjects' MEGs and the differences were statistically significant (p<0.01). In order to reduce the dimension of the LZ complexity results, a principal components analysis (PCA) was applied, and only the first principal component was retained. The first component score from PCA was graphically analysed using a box plot and a receiver-operating characteristic (ROC) curve. A specificity of 85.71%, a sensitivity of 80.95% and an area under the ROC curve of 0.9002 were obtained. These preliminary results suggest that cognitive dysfunction in AD is associated with a decreased LZ complexity in the MEG signals.
|Divisions :||Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences|
|Date :||November 2006|
|Identification Number :||10.1016/j.medengphy.2006.01.003|
|Uncontrolled Keywords :||Aged, Aged, 80 and over, Alzheimer Disease, Case-Control Studies, Data Interpretation, Statistical, Diagnosis, Computer-Assisted, Humans, Magnetoencephalography, Middle Aged, Nonlinear Dynamics, Pattern Recognition, Automated, Principal Component Analysis, ROC Curve, Sensitivity and Specificity|
|Additional Information :||NOTICE: this is the author’s version of a work that was accepted for publication in Medical Engineering and Physics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Medical Engineering and Physics, 28(9),Noivember 2006, DOI 10.1016/j.medengphy.2006.01.003.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||25 Jan 2012 01:16|
|Last Modified :||23 Sep 2013 18:56|
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