Document Detail


Regional analysis of spontaneous MEG rhythms in patients with Alzheimer's disease using spectral entropies.
MedLine Citation:
PMID:  17994279     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Alzheimer's disease (AD) is the most common form of dementia. Ageing is the greatest known risk factor for this disorder. Therefore, the prevalence of AD is expected to increase in western countries due to the rise in life expectancy. Nowadays, a low diagnosis accuracy is reached, but an early and accurate identification of AD should be attempted. In this sense, only a few studies have focused on the magnetoencephalographic (MEG) AD patterns. This work represents a new effort to explore the ability of three entropies from information theory to discriminate between spontaneous MEG rhythms from 20 AD patients and 21 controls. The Shannon (SSE), Tsallis (TSE), and Rényi (RSE) spectral entropies were calculated from the time-frequency distribution of the power spectral density (PSD). The entropies provided statistically significant lower values for AD patients than for controls in all brain regions (p < 0.0005). This fact suggests a significant loss of irregularity in AD patients' MEG activity. Maximal accuracy of 87.8% was achieved by both the TSE and RSE (90.0%, sensitivity; 85.7%, specificity). The statistically significant results obtained by both the extensive (SSE and RSE) and non-extensive (TSE) spectral entropies suggest that AD could disturb long and short-range interactions causing an abnormal brain function.
Authors:
Jesús Poza; Roberto Hornero; Javier Escudero; Alberto Fernández; Clara I Sánchez
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't     Date:  2007-11-10
Journal Detail:
Title:  Annals of biomedical engineering     Volume:  36     ISSN:  1573-9686     ISO Abbreviation:  Ann Biomed Eng     Publication Date:  2008 Jan 
Date Detail:
Created Date:  2007-12-18     Completed Date:  2008-01-16     Revised Date:  2013-05-30    
Medline Journal Info:
Nlm Unique ID:  0361512     Medline TA:  Ann Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  141-52     Citation Subset:  IM    
Affiliation:
Biomedical Engineering Group, Department T.S.C.I.T., E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011, Valladolid, Spain. jespoz@tel.uva.es
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MeSH Terms
Descriptor/Qualifier:
Aged
Algorithms*
Alzheimer Disease / diagnosis*
Diagnosis, Computer-Assisted / methods*
Entropy
Female
Humans
Magnetoencephalography / methods*
Male
Oscillometry / methods*
Reproducibility of Results
Sensitivity and Specificity

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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