Markus Waser

Quantitative EEG markers relate to Alzheimer’s disease severity in the Prospective Dementia Registry Austria (PRODEM)

OBJECTIVE To investigate which single quantitative electro-encephalographic (QEEG) marker or which combination of markers correlates best with Alzheimers disease (AD) severity as measured by the Mini-Mental State Examination (MMSE). METHODS We compared quantitative EEG markers for slowing (relative band powers), synchrony (coherence, canonical correlation, Granger causality) and complexity (auto-mutual information, Shannon/Tsallis entropy) in 118 AD patients from the multi-centric study PRODEM Austria. Signal spectra were determined using an indirect spectral estimator. Analyses were adjusted for age, sex, duration of dementia, and level of education. RESULTS For the whole group (39 possible, 79 probable AD cases) MMSE scores explained 33% of the variations in relative theta power during face encoding, and 31% of auto-mutual information in resting state with eyes closed. MMSE scores explained also 25% of the overall QEEG factor. This factor was thus subordinate to individual markers. In probable AD, QEEG coefficients of determination were always higher than in the whole group, where MMSE scores explained 51% of the variations in relative theta power. CONCLUSIONS Selected QEEG markers show strong associations with AD severity. Both cognitive and resting state should be used for QEEG assessments. SIGNIFICANCE Our data indicate theta power measured during face-name encoding to be most closely related to AD severity.

Removing cardiac interference from the electroencephalogram using a modified Pan-Tompkins algorithm and linear regression

Cardiac interference can alter the results of quantitative electroencephalograms (qEEG) used for medical diagnoses. The methods currently employed for the automated removal of cardiac interference, which rely solely on the electroencephalogram (EEG), are susceptible to non-cardiac interference commonly encountered in EEGs. Methods that rely on the electrocardiogram (ECG) - besides being unreliable when non-cardiac artifacts corrupt the ECG - either assume periodicity of the cardiac (QRS) peaks or alter uncorrupted EEG segments. This paper proposes a robust method for the automated removal of cardiac interference from EEGs by identifying QRS peaks in the ECG without assuming periodicity. Artificial signals consisting only of QRS peaks and the zero-lines in between are computed. Linear regression of the EEG channels on the “QRS signals” removes cardiac interference without altering uncorrupted EEG segments. The QRS-based regression method was tested on 30 multi-channel EEGs exhibiting cardiac interference of elderly subjects (15 male, 15 female). Achieving a correction rate of 80%, the QRS-based regression method has proved effective in removing cardiac interference from the EEG even in presence of additional non-cardiac interference in the EEG.

Differential diagnosis between patients with probable Alzheimer’s disease, Parkinson’s disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalographic features

The objective of this work was to develop and evaluate a classifier for differentiating probable Alzheimer’s disease (AD) from Parkinson’s disease dementia (PDD) or dementia with Lewy bodies (DLB) and from frontotemporal dementia, behavioral variant (bvFTD) based on quantitative electroencephalography (QEEG). We compared 25 QEEG features in 61 dementia patients (20 patients with probable AD, 20 patients with PDD or probable DLB (DLBPD), and 21 patients with bvFTD). Support vector machine classifiers were trained to distinguish among the three groups. Out of the 25 features, 23 turned out to be significantly different between AD and DLBPD, 17 for AD versus bvFTD, and 12 for bvFTD versus DLBPD. Using leave-one-out cross validation, the classification achieved an accuracy, sensitivity, and specificity of 100% using only the QEEG features Granger causality and the ratio of theta and beta1 band powers. These results indicate that classifiers trained with selected QEEG features can provide a valuable input in distinguishing among AD, DLB or PDD, and bvFTD patients. In this study with 61 patients, no misclassifications occurred. Therefore, further studies should investigate the potential of this method to be applied not only on group level but also in diagnostic support for individual subjects.

Quantitative EEG Markers of Entropy and Auto Mutual Information in Relation to MMSE Scores of Probable Alzheimer’s Disease Patients

Analysis of nonlinear quantitative EEG (qEEG) markers describing complexity of signal in relation to severity of Alzheimer’s disease (AD) was the focal point of this study. In this study, 79 patients diagnosed with probable AD were recruited from the multi-centric Prospective Dementia Database Austria (PRODEM). EEG recordings were done with the subjects seated in an upright position in a resting state with their eyes closed. Models of linear regressions explaining disease severity, expressed in Mini Mental State Examination (MMSE) scores, were analyzed by the nonlinear qEEG markers of auto mutual information (AMI), Shannon entropy (ShE), Tsallis entropy (TsE), multiscale entropy (MsE), or spectral entropy (SpE), with age, duration of illness, and years of education as co-predictors. Linear regression models with AMI were significant for all electrode sites and clusters, where R 2 is 0.46 at the electrode site C3, 0.43 at Cz, F3, and central region, and 0.42 at the left region. MsE also had significant models at C3 with R 2 > 0.40 at scales τ = 5 and τ = 6 . ShE and TsE also have significant models at T7 and F7 with R 2 > 0.30 . Reductions in complexity, calculated by AMI, SpE, and MsE, were observed as the MMSE score decreased.

Quantifying synchrony patterns in the EEG of Alzheimer’s patients with linear and non-linear connectivity markers

We analyzed the relation of several synchrony markers in the electroencephalogram (EEG) and Alzheimer’s disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores. The study sample consisted of 79 subjects diagnosed with probable AD. All subjects were participants in the PRODEM-Austria study. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. We employed quadratic least squares regression to describe the relation between MMSE and the EEG markers. Factor analysis was used for estimating a potentially lower number of unobserved synchrony factors. These common factors were then related to MMSE scores as well. Most markers displayed an initial increase of EEG synchrony with MMSE scores from 26 to 21 or 20, and a decrease below. This effect was most prominent during the cognitive task and may be owed to cerebral compensatory mechanisms. Factor analysis provided interesting insights in the synchrony structures and the first common factors were related to MMSE scores with coefficients of determination up to 0.433. We conclude that several of the proposed EEG markers are related to AD severity for the overall sample with a wide dispersion for individual subjects. Part of these fluctuations may be owed to fluctuations and day-to-day variability associated with MMSE measurements. Our study provides a systematic analysis of EEG synchrony based on a large and homogeneous sample. The results indicate that the individual markers capture different aspects of EEG synchrony and may reflect cerebral compensatory mechanisms in the early stages of AD.

Robust, automatic real-time monitoring of the time course of the individual alpha frequency in the time and frequency domain

We analyzed three different approaches to automatic real-time monitoring of the time course of individual alpha frequencies (IAFs) of the human electro-encephalograms. Fast Fourier transform and wavelet transform were compared to classical automated cycle counting in the time domain. With fast Fourier and wavelet transform, test results with healthy adult subjects, demented and psychiatric patients revealed typical short-term variations of the instantaneous IAFs of about ± 2 Hz. When cycles were counted in the time domain, however, variations of only ± 1 Hz were recorded. Thus, IAF measurement in the time domain appears to be particularly suitable. We also observed long-term IAF trends that typically amounted to about ± 0.5 to ± 1.0 Hz. Therefore, our hypothesis is that the IAF does not constitute an intra-individual constant but varies with time and cognitive state. Our fully automatic real-time signal-processing procedure includes pre-processing for artifact detection and for localization of segments with synchronized alpha oscillations where the IAF should preferably be measured.

Predicting rapid cognitive decline in Alzheimer's disease patients using quantitative EEG markers and neuropsychological test scores

Alzheimers Disease (AD) can take different courses: some patients remain relatively stable while others decline rapidly within a given period of time. Losing more than 3 Mini-Mental State Examination (MMSE) points in one year is classified as rapid cognitive decline (RCD). This study used neuropsychological test scores and quantitative EEG (QEEG) markers obtained at a baseline examination to identify if an AD patient will be suffering from RCD. Data from 68 AD patients of the multi-centric cohort study PRODEM-Austria were applied. 15 of the patients were classified into the RCD group. RCD versus non-RCD support vector machine (SVM) classifiers using QEEG markers as predictors obtained 72.1% and 77.9% accuracy ratings based on leave-one-out validation. Adding neuropsychological test scores of Boston Naming Test improved the classifier to 80.9% accuracy, 80% sensitivity, and 81.1% specificity. These results indicate that QEEG markers together with neuropsychological test scores can be used as RCD predictors.

Using static and dynamic canonical correlation coefficients as quantitative EEG markers for Alzheimer's disease severity

We analyzed the relation between Alzheimers disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores and quantitative electroencephalo-graphic (qEEG) markers that were derived from canonical correlation analysis. This allowed an investigation of EEG synchrony between groups of EEG channels. In this study, we applied the data from 79 participants in the multi-centric cohort study PRODEM-Austria with probable AD. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. A quadratic regression model was used to describe the relation between MMSE and the qEEG synchrony markers. This relation was most significant in the δ and θ frequency bands in resting state, and between left-hemispheric central, temporal and parietal channel groups during the cognitive task. Here, the MMSE explained up to 40% of the qEEG markers variation. QEEG markers showed an ambiguous trend, i.e. an increase of EEG synchrony in the initial stage of AD (MMSE>20) and a decrease in later stages. This effect could be caused by compensatory brain mechanisms. We conclude that the proposed qEEG markers are closely related to AD severity. Despite the ambiguous trend and the resulting diagnostic ambiguity, the qEEG markers could provide aid in the diagnostics of early-stage AD.

Bayesian Gaussian Process Classification from Event-Related Brain Potentials in Alzheimer’s Disease

Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in Alzheimer’s disease (AD) and might qualify as non-invasive and cost-effective markers to facilitate the objectivization of AD assessment in daily clinical practice. Lately, the combination of multivariate pattern analysis (MVPA) and Gaussian process classification (GPC) has gained interest in the neuroscientific community. Here, we demonstrate how a MVPA-GPC approach can be applied to electrophysiological data. Furthermore, in order to account for the temporal information of ERPs, we develop a novel method that integrates interregional synchrony of ERP time signatures. By using real-life ERP recordings of a prospective AD cohort study (PRODEM), we empirically investigate the usefulness of the proposed framework to build neurophysiological markers for single subject classification tasks. GPC outperforms the probabilistic reference method in both tasks, with the highest AUC overall (0.802) being achieved using the new spatiotemporal method in the prediction of rapid cognitive decline.

Electroencephalographic complexity markers explain neuropsychological test scores in Alzheimer's disease

We investigated the correlation of Alzheimers disease (AD) severity as measured by the Mini-Mental State Examination (MMSE) to the signal complexity measures auto-mutual information, Shannon entropy and Tsallis entropy in 79 patients with probable AD from the multi-centric Prospective Dementia Database Austria (PRODEM). Using quadratic (linear) regressions, auto-mutual information explained up to 48% (43%), Shannon entropy up to 48% (37%) and Tsallis entropy up to 49% (35%) of the variations in MMSE scores, all at left temporal (T7) electrode site. The steepest slope of the linear regression was found for auto-mutual information (Δy/Δx = 36). For Shannon and Tsallis entropy, slopes were less steep. Comparing to traditional slowing measures, complexity measures yielded higher coefficients of determination. We conclude that auto-mutual information is well suited to characterize disease severity in mild to moderate AD.