Literature DB >> 11488226

Diagnostic value of quantitative EEG in Alzheimer's disease.

K Bennys1, G Rondouin, C Vergnes, J Touchon.   

Abstract

The aim of this study was to determine the performance of several spectral indices of the EEG (ratios between fast and slow EEG activities) as descriptors of the EEG changes occurring at the onset and during the evolution of Alzheimer's disease (AD). These indices were calculated from quantitative analysis of EEGs recorded in AD patients and from a matched non-demented group of control subjects. One advantage of such indices is to be independent of the absolute value of power spectral densities, which may vary from subject to subject, another being to take into account fast EEG activities. Conventional statistic tests and Receiver Operating Curves (ROC) analysis were performed upon these data to determine the accuracy of the power ratios to discriminate a) between controls and patients (i.e., to detect dementia) and b) between subgroups of patients defined according to the Global Deterioration Scale of Reisberg (GDS). The defined ratios provided a good classification of AD patients for all cerebral regions except the frontal areas, because of eye movement artefacts; the results confirm the increase in slow activities and the concomitant decrease in fast activities early in AD patients. Moreover, our results demonstrate that these indices are adapted tools to perform a good discrimination between demented and non-demented patients in routine clinical practice. We therefore propose the use of these EEG power ratios to discriminate between different stages of Alzheimer's disease, and to perform long-term monitoring of AD patients.

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Mesh:

Year:  2001        PMID: 11488226     DOI: 10.1016/s0987-7053(01)00254-4

Source DB:  PubMed          Journal:  Neurophysiol Clin        ISSN: 0987-7053            Impact factor:   3.734


  29 in total

1.  Occipital sources of resting-state alpha rhythms are related to local gray matter density in subjects with amnesic mild cognitive impairment and Alzheimer's disease.

Authors:  Claudio Babiloni; Claudio Del Percio; Marina Boccardi; Roberta Lizio; Susanna Lopez; Filippo Carducci; Nicola Marzano; Andrea Soricelli; Raffaele Ferri; Antonio Ivano Triggiani; Annapaola Prestia; Serenella Salinari; Paul E Rasser; Erol Basar; Francesco Famà; Flavio Nobili; Görsev Yener; Derya Durusu Emek-Savaş; Loreto Gesualdo; Ciro Mundi; Paul M Thompson; Paolo M Rossini; Giovanni B Frisoni
Journal:  Neurobiol Aging       Date:  2014-09-21       Impact factor: 4.673

2.  Power spectral density and coherence analysis of Alzheimer's EEG.

Authors:  Ruofan Wang; Jiang Wang; Haitao Yu; Xile Wei; Chen Yang; Bin Deng
Journal:  Cogn Neurodyn       Date:  2014-12-16       Impact factor: 5.082

3.  Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer's disease.

Authors:  Robi Polikar; Apostolos Topalis; Deborah Green; John Kounios; Christopher M Clark
Journal:  Comput Biol Med       Date:  2006-09-20       Impact factor: 4.589

4.  Exploration of EEG features of Alzheimer's disease using continuous wavelet transform.

Authors:  Parham Ghorbanian; David M Devilbiss; Terry Hess; Allan Bernstein; Adam J Simon; Hashem Ashrafiuon
Journal:  Med Biol Eng Comput       Date:  2015-04-12       Impact factor: 2.602

5.  Multiple characteristics analysis of Alzheimer's electroencephalogram by power spectral density and Lempel-Ziv complexity.

Authors:  Xiaokun Liu; Chunlai Zhang; Zheng Ji; Yi Ma; Xiaoming Shang; Qi Zhang; Wencheng Zheng; Xia Li; Jun Gao; Ruofan Wang; Jiang Wang; Haitao Yu
Journal:  Cogn Neurodyn       Date:  2015-11-12       Impact factor: 5.082

6.  Sleep and EEG Power Spectral Analysis in Three Transgenic Mouse Models of Alzheimer's Disease: APP/PS1, 3xTgAD, and Tg2576.

Authors:  Brianne A Kent; Stephen M Strittmatter; Haakon B Nygaard
Journal:  J Alzheimers Dis       Date:  2018       Impact factor: 4.472

7.  EEG spectral power abnormalities and their relationship with cognitive dysfunction in patients with Alzheimer's disease and type 2 diabetes.

Authors:  Christopher S Y Benwell; Paula Davila-Pérez; Peter J Fried; Richard N Jones; Thomas G Travison; Emiliano Santarnecchi; Alvaro Pascual-Leone; Mouhsin M Shafi
Journal:  Neurobiol Aging       Date:  2019-10-14       Impact factor: 4.673

8.  Fully automated discrimination of Alzheimer's disease using resting-state electroencephalography signals.

Authors:  Yue Ding; Yinxue Chu; Meng Liu; Zhenhua Ling; Shijin Wang; Xin Li; Yunxia Li
Journal:  Quant Imaging Med Surg       Date:  2022-02

9.  Electroencephalographic rhythms in Alzheimer's disease.

Authors:  Roberta Lizio; Fabrizio Vecchio; Giovanni B Frisoni; Raffaele Ferri; Guido Rodriguez; Claudio Babiloni
Journal:  Int J Alzheimers Dis       Date:  2011-05-12

Review 10.  The Role of Cognitive Reserve in Alzheimer's Disease and Aging: A Multi-Modal Imaging Review.

Authors:  Arianna Menardi; Alvaro Pascual-Leone; Peter J Fried; Emiliano Santarnecchi
Journal:  J Alzheimers Dis       Date:  2018       Impact factor: 4.472

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