Document Detail


Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure.
MedLine Citation:
PMID:  16122963     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In this study we have investigated the electroencephalogram (EEG) background activity in patients with Alzheimer's disease (AD) using non-linear analysis methods. We calculated the Lempel-Ziv (LZ) complexity - applying two different sequence conversion methods - and the central tendency measure (CTM) of the EEG in 11 AD patients and 11 age-matched control subjects. CTM quantifies the degree of variability, while LZ complexity reflects the arising rate of new patterns along with the EEG time series. We did not find significant differences between AD patients and control subjects' EEGs with CTM. On the other hand, AD patients had significantly lower LZ complexity values (p<0.01) at electrodes P3 and O1 with a two-symbol sequence conversion, and P3, P4, O1 and T5 using three symbols. Our results show a decreased complexity of EEG patterns in AD patients. In addition, we obtained 90.9% sensitivity and 72.7% specificity at O1, and 72.7% sensitivity and 90.9% specificity at P3 and P4. These findings suggest that LZ complexity may contribute to increase the insight into brain dysfunction in AD in ways which are not possible with more classical and conventional statistical methods.
Authors:
Daniel Abásolo; Roberto Hornero; Carlos Gómez; María García; Miguel López
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Publication Detail:
Type:  Controlled Clinical Trial; Journal Article; Research Support, Non-U.S. Gov't     Date:  2005-08-24
Journal Detail:
Title:  Medical engineering & physics     Volume:  28     ISSN:  1350-4533     ISO Abbreviation:  Med Eng Phys     Publication Date:  2006 May 
Date Detail:
Created Date:  2006-03-20     Completed Date:  2006-06-01     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9422753     Medline TA:  Med Eng Phys     Country:  England    
Other Details:
Languages:  eng     Pagination:  315-22     Citation Subset:  IM    
Affiliation:
E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain. danaba@tel.uva.es
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MeSH Terms
Descriptor/Qualifier:
Aged
Algorithms*
Alzheimer Disease / diagnosis*
Artificial Intelligence*
Diagnosis, Computer-Assisted / methods*
Electroencephalography / methods*
Female
Humans
Male
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity

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


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