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      Discrimination of mild cognitive impairment and Alzheimer's disease using transfer entropy measures of scalp EEG.

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          Abstract

          Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimer's disease (AD). This study investigates the potential of measures of transfer entropy in scalp EEG for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls, 16 MCI, and 17 early AD-are examined. The mean temporal delays corresponding to peaks in inter-regional transfer entropy are computed and used as features to discriminate between the three groups of participants. Three-way classification schemes based on binary support vector machine models demonstrate overall discrimination accuracies of 91.7- 93.8%, depending on the protocol condition. These results demonstrate the potential for EEG transfer entropy measures as biomarkers in identifying early MCI and AD. Moreover, the analyses based on short data segments (two minutes) render the method practical for a primary care setting.

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          Classification by pairwise coupling

          Trevor J. Hastie, Robert Tibshirani (1998)
          Article 0 comments Cited 124 times – based on 0 reviews     Review now
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            Discrimination of Alzheimer's disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study.

            C. Huang, L-O Wahlund, T Dierks … (2000)
            The spatial aspects of brain electrical activity can be assessed by equivalent EEG frequency band generators. We aimed to describe alterations of these EEG generators in Alzheimer's disease (AD) and healthy aging and whether they could serve as predictive markers of AD in subjects at risk. The amplitude and 3-dimensional localization of equivalent EEG sources were evaluated using FFT dipole approximation in 38 mild AD patients, 31 subjects with mild cognitive impairment (MCI) and 24 healthy control subjects. AD patients showed an increase of delta and theta global field power (GFP), which corresponds to the generalized EEG amplitude, as well as a reduction of alpha GFP when compared to the controls. A decrease of alpha and beta GFP was found in AD patients, as compared to the MCI subjects. With respect to topography in the antero-posterior direction, sources of alpha and beta activity shifted more anteriorly in AD patients compared to both the controls and MCI subjects. No significant difference was found between MCI and controls. Combined alpha and theta GFP were the best discriminating variables between AD patients and controls (84% correct classification) and AD and MCI subjects (78% correctly classified). MCI subjects were followed longitudinally (25 months on average) in order to compare differences in baseline EEG variables between MCI subjects who progressed to AD (PMCI) and those who remained stable (SMCI). Compared to SMCI, PMCI had decreased alpha GFP and a more anterior localization of sources of theta, alpha and beta frequency. In a linear discriminant analysis applied on baseline values of the two MCI subgroups, the best predictor of future development of AD was found to be antero-posterior localization of alpha frequency. FFT dipole approximation and frequency analysis performed by conventional FFT showed comparable classification accuracy between the studied groups. We conclude that localization and amplitude of equivalent EEG sources could be promising markers of early AD.
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              Diagnosis of Alzheimer's disease from EEG signals: where are we standing?

              Justin Dauwels, François Vialatte, Andrzej Cichocki (2010)
              This paper reviews recent progress in the diagnosis of Alzheimer's disease (AD) from electroencephalograms (EEG). Three major effects of AD on EEG have been observed: slowing of the EEG, reduced complexity of the EEG signals, and perturbations in EEG synchrony. In recent years, a variety of sophisticated computational approaches has been proposed to detect those subtle perturbations in the EEG of AD patients. The paper first describes methods that try to detect slowing of the EEG. Next the paper deals with several measures for EEG complexity, and explains how those measures have been used to study fluctuations in EEG complexity in AD patients. Then various measures of EEG synchrony are considered in the context of AD diagnosis. Also the issue of EEG pre-processing is briefly addressed. Before one can analyze EEG, it is necessary to remove artifacts due to for example head and eye movement or interference from electronic equipment. Pre-processing of EEG has in recent years received much attention. In this paper, several state-of-the-art pre-processing tech- niques are outlined, for example, based on blind source separation and other non-linear filtering paradigms. In addition, the paper outlines opportunities and limitations of computational approaches for diagnosing AD based on EEG. At last, future challenges and open problems are discussed.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): J Healthc Eng
                Title: Journal of healthcare engineering
                Publisher: Multi-Science Publishing Co. Ltd.
                ISSN: 2040-2295
                ISSN (Print): 2040-2295
                Publication date (Electronic): 2015
                Volume: 6
                Issue: 1
                Affiliations
                [1 ] Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA.
                [2 ] Oak Ridge Nation Laboratory, Oak Ridge, TN, USA.
                [3 ] Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA Department of Neurology, University of Kentucky College of Medicine, Lexington, KY, USA.
                [4 ] Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA Department of Behavioral Science, University of Kentucky College of Medicine, Lexington, KY, USA.
                Article
                Publisher Item ID: A2527308N1326725 Mid ID: NIHMS673671
                DOI: 10.1260/2040-2295.6.1.55
                PMC ID: 4385710
                PubMed ID: 25708377
                SO-VID: 3b8a0f19-4fc8-48e5-8bcd-15f5d166ee01
                History

                Keywords: EEG-based diagnosis,early Alzheimer's disease,mild cognitive impairment,transfer entropy
                Data availability:
                Keywords: EEG-based diagnosis, early Alzheimer's disease, mild cognitive impairment, transfer entropy

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