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Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages...

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Autores principales: Vialatte, François-B., Dauwels, Justin, Maurice, Monique, Musha, Toshimitsu, Cichocki, Andrzej
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE-Hindawi Access to Research 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3109519/
https://www.ncbi.nlm.nih.gov/pubmed/21660242
http://dx.doi.org/10.4061/2011/259069
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author Vialatte, François-B.
Dauwels, Justin
Maurice, Monique
Musha, Toshimitsu
Cichocki, Andrzej
author_facet Vialatte, François-B.
Dauwels, Justin
Maurice, Monique
Musha, Toshimitsu
Cichocki, Andrzej
author_sort Vialatte, François-B.
collection PubMed
description Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz), α(1) (7.5–9.5 Hz), α(2) (9.5–12.5 Hz), and β (12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.
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spelling pubmed-31095192011-06-09 Improving the Specificity of EEG for Diagnosing Alzheimer's Disease Vialatte, François-B. Dauwels, Justin Maurice, Monique Musha, Toshimitsu Cichocki, Andrzej Int J Alzheimers Dis Research Article Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz), α(1) (7.5–9.5 Hz), α(2) (9.5–12.5 Hz), and β (12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD. SAGE-Hindawi Access to Research 2011-05-30 /pmc/articles/PMC3109519/ /pubmed/21660242 http://dx.doi.org/10.4061/2011/259069 Text en Copyright © 2011 François-B. Vialatte et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Vialatte, François-B.
Dauwels, Justin
Maurice, Monique
Musha, Toshimitsu
Cichocki, Andrzej
Improving the Specificity of EEG for Diagnosing Alzheimer's Disease
title Improving the Specificity of EEG for Diagnosing Alzheimer's Disease
title_full Improving the Specificity of EEG for Diagnosing Alzheimer's Disease
title_fullStr Improving the Specificity of EEG for Diagnosing Alzheimer's Disease
title_full_unstemmed Improving the Specificity of EEG for Diagnosing Alzheimer's Disease
title_short Improving the Specificity of EEG for Diagnosing Alzheimer's Disease
title_sort improving the specificity of eeg for diagnosing alzheimer's disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3109519/
https://www.ncbi.nlm.nih.gov/pubmed/21660242
http://dx.doi.org/10.4061/2011/259069
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