The value of quantitative EEG in differential diagnosis of Alzheimer's disease and subcortical vascular dementia

Gaweł, Malgorzata; Zalewska, Ewa; Szmidt-Salkowska, Elzbieta; Kowalski, J.

doi:10.1016/j.jns.2009.02.332

Cited by 65 publications

(38 citation statements)

References 32 publications

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“…Selected parameters of qEEG could also be used in addition to differential diagnosis between AD and subcortical vascular dementia with the limitation of the same level of dementia severity [59]. …”

Section: Eeg and Ad Differential Diagnosismentioning

confidence: 99%

Electroencephalogram and Alzheimer’s Disease: Clinical and Research Approaches

Tsolaki

Kazis

Kompatsiaris

et al. 2014

International Journal of Alzheimer's Disease

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Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by cognitive deficits, problems in activities of daily living, and behavioral disturbances. Electroencephalogram (EEG) has been demonstrated as a reliable tool in dementia research and diagnosis. The application of EEG in AD has a wide range of interest. EEG contributes to the differential diagnosis and the prognosis of the disease progression. Additionally such recordings can add important information related to the drug effectiveness. This review is prepared to form a knowledge platform for the project entitled “Cognitive Signal Processing Lab,” which is in progress in Information Technology Institute in Thessaloniki. The team tried to focus on the main research fields of AD via EEG and recent published studies.

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Section: Eeg and Ad Differential Diagnosismentioning

confidence: 99%

Electroencephalogram and Alzheimer’s Disease: Clinical and Research Approaches

Tsolaki

Kazis

Kompatsiaris

et al. 2014

International Journal of Alzheimer's Disease

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“…EEG was for a long time discarded as a dementia diagnostic tool due to low specificity [8]. However, recent studies using modern statistical methods have demonstrated its usefulness for differentiating AD from dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD) [9], frontotemporal lobar degeneration (FTLD) [10], and vascular dementia (VaD) [11]. Novel methods such as quantitative EEG in combination with statistical pattern recognition (qEEG-SPR) facilitate data analysis and interpretation.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalography Is a Good Complement to Currently Established Dementia Biomarkers

Ferreira

Jelić

Cavallin

et al. 2016

Dement Geriatr Cogn Disord

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Background/Aims: Dementia biomarkers that are accessible and easily applicable in nonspecialized clinical settings are urgently needed. Quantitative electroencephalography (qEEG) is a good candidate, and the statistical pattern recognition (SPR) method has recently provided promising results. We tested the diagnostic value of qEEG-SPR in comparison to cognition, structural imaging, and cerebrospinal fluid (CSF) biomarkers. Methods: A total of 511 individuals were recruited from the multicenter NORD EEG study [141 healthy controls, 64 subjective cognitive decline, 124 mild cognitive impairment, 135 Alzheimer's disease (AD), 15 dementia with Lewy bodies/Parkinson's disease with dementia (DLB/PDD), 32 other dementias]. The EEG data were recorded in a standardized way. Structural imaging data were visually rated using scales of atrophy in the medial temporal, frontal, and posterior cortex. Results: qEEG-SPR outperformed structural imaging, cognition, and CSF biomarkers in DLB/PDD diagnosis, outperformed structural imaging in AD diagnosis, and improved the differential diagnosis of AD. In addition, qEEG-SPR allowed differentiation of two clinically different AD subtypes. Conclusion: Adding qEEG to the diagnostic workup substantially increases the detection of AD pathology even in pre-dementia stages and improves differential diagnosis. EEG could serve as a good complement to currently established dementia biomarkers since it is cheap, noninvasive, and extensively applied outside academic centers.

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“…This phenomenon has also been described by other studies and is associated with general cognitive decline (Dierks et al, 1995; Kwak, 2006; Babiloni et al, 2011b; Fraga et al, 2013). AD and VaD patients are commonly described as having reduced frequency of the posterior dominant rhythm compared with healthy subjects (Babiloni et al, 2004; Gawel et al, 2009). When comparing AD with VaD patients, the literature reports two core electrophysiological differences between these two groups: AD patients have lower posterior alpha power when compared with VaD; the lower frequencies power appears to be higher in VaD compared to AD (Signorino et al, 1995, 1996; Babiloni et al, 2004, 2011a).…”

Section: Introductionmentioning

confidence: 99%

Regularized Linear Discriminant Analysis of EEG Features in Dementia Patients

Neto

Bießmann

Aurlien

et al. 2016

Front. Aging Neurosci.

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The present study explores if EEG spectral parameters can discriminate between healthy elderly controls (HC), Alzheimer’s disease (AD) and vascular dementia (VaD) using. We considered EEG data recorded during normal clinical routine with 114 healthy controls (HC), 114 AD, and 114 VaD patients. The spectral features extracted from the EEG were the absolute delta power, decay from lower to higher frequencies, amplitude, center and dispersion of the alpha power and baseline power of the entire frequency spectrum. For discrimination, we submitted these EEG features to regularized linear discriminant analysis algorithm with a 10-fold cross-validation. To check the consistency of the results obtained by our classifiers, we applied bootstrap statistics. Four binary classifiers were used to discriminate HC from AD, HC from VaD, AD from VaD, and HC from dementia patients (AD or VaD). For each model, we measured the discrimination performance using the area under curve (AUC) and the accuracy of the cross-validation (cv-ACC). We applied this procedure using two different sets of predictors. The first set considered all the features extracted from the 22 channels. For the second set of features, we automatically rejected features poorly correlated with their labels. Fairly good results were obtained when discriminating HC from dementia patients with AD or VaD (AUC = 0.84). We also obtained AUC = 0.74 for discrimination of AD from HC, AUC = 0.77 for discrimination of VaD from HC, and finally AUC = 0.61 for discrimination of AD from VaD. Our models were able to separate HC from dementia patients, and also and to discriminate AD from VaD above chance. Our results suggest that these features may be relevant for the clinical assessment of patients with dementia.

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The value of quantitative EEG in differential diagnosis of Alzheimer's disease and subcortical vascular dementia

Cited by 65 publications

References 32 publications

Electroencephalogram and Alzheimer’s Disease: Clinical and Research Approaches

Electroencephalogram and Alzheimer’s Disease: Clinical and Research Approaches

Electroencephalography Is a Good Complement to Currently Established Dementia Biomarkers

Regularized Linear Discriminant Analysis of EEG Features in Dementia Patients

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