Linked Life Data

17994279

Source:http://linkedlifedata.com/resource/pubmed/id/17994279

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2007-12-18
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1521-6047
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
36
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
141-52
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Regional analysis of spontaneous MEG rhythms in patients with Alzheimer's disease using spectral entropies.
pubmed: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
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies