Power frequency and wavelet characteristics in differentiating between normal and Alzheimer EEG

Sriraja, Y.; Baker, Mary C.; Petrosian, A.

doi:10.1109/iembs.2002.1134380

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…Nonetheless, the diagnosis of AD from EEG data is still open research topic, and it comes without surprise the wealth of methods proposed in the medical and related literature. These methods, based on Fast Fourier Transform (FFT) [17][18][19], Wavelet Transform (WT) [20][21][22][23], Phase-Space Reconstruction [24][25][26], Eigenvector Methods (EMs) [27,28], Time Frequency Distributions (TFDs) [29], and the Auto-Regressive Method (ARM) [30], generally require from the input signal one or more of the following assumptions: stationarity, high time or frequency resolution, and/or a high signal-to-noise ratio.…”

Section: Introductionmentioning

confidence: 99%

Quantile graphs for EEG-based diagnosis of Alzheimer’s disease

et al. 2020

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Known as a degenerative and progressive dementia, Alzheimer's disease (AD) affects about 25 million elderly people around the world. This illness results in a decrease in the productivity of people and places limits on their daily lives. Electroencephalography (EEG), in which the electrical brain activity is recorded in the form of time series and analyzed using signal processing techniques, is a well-known neurophysiological AD biomarker. EEG is noninvasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Here, we present an original approach based on the use of quantile graphs (QGs) for classifying EEG data. QGs map frequency, amplitude, and correlation characteristics of a time series (such as the EEG data of an AD patient) into the topological features of a network. The five topological network metrics used here-clustering coefficient, mean jump length, betweenness centrality, modularity, and Laplacian Estrada index-showed that the QG model can distinguish healthy subjects from AD patients, with open or closed eyes. The QG method also indicates which channels (corresponding to 19 different locations on the patients' scalp) provide the best discriminating power. Furthermore, the joint analysis of delta, theta, alpha, and beta wave results indicate that all AD patients under study display clear symptoms of the disease and may have it in its late stage, a diagnosis known a priori and supported by our study. Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs.

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Section: Introductionmentioning

confidence: 99%

Quantile graphs for EEG-based diagnosis of Alzheimer’s disease

et al. 2020

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“…For individual AD-specific diagnosis, there have been very few studies that use an appropriate time-frequency analysis, such as discrete wavelet transform, followed by neural network classification. The results of these primarily pilot studies, such as [46,47], and including our previous efforts [48,49] can be summarized as only limited success, due to several reasons: relatively small study cohort with typically 10-30 patients, not targeting diagnosis at the earliest stages, suboptimal selection of classifier model and/or its parameters, as well as the sheer inherent difficulty of the problem. The results therefore remain largely inconclusive.…”

Section: Introductionmentioning

confidence: 99%

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

Polikar

Topalis

Green

et al. 2007

Computers in Biology and Medicine

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Early diagnosis of Alzheimer's disease (AD) is becoming an increasingly important healthcare concern. Prior approaches analyzing event-related potentials (ERPs) had varying degrees of success, primarily due to smaller study cohorts, and the inherent difficulty of the problem. A new effort using multiresolution analysis of ERPs is described. Distinctions of this study include analyzing a larger cohort, comparing different wavelets and different frequency bands, using ensemble-based decisions and, most importantly, aiming the earliest possible diagnosis of the disease. Surprising yet promising outcomes indicate that ERPs in response to novel sounds of oddball paradigm may be more reliable as a biomarker than the more commonly used responses to target sounds.

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“…Despite its now mature history, studies applying time-frequency techniques, such as wavelets, to ERPs have only recently started, and mostly on non AD related studies designed specifically for P300 analysis [4,5]. Studies directly targeting AD diagnosis using wavelet analysis, have been even more rare with limited success, in part due to lack of a large study cohort (e.g., 6 in [6]); the results therefore remain largely inconclusive.…”

Section: A Eeg Analysis For Ad Diagnosismentioning

confidence: 96%

“…The normalized composite error B t is obtained in step 6, which is then used for updating the distribution weights assigned to individual instances. Equation (6) indicates that the distribution weights of the instances correctly classified by the composite hypothesis H t are reduced by a factor of B t <1. Effectively, this increases the weights of the misclassified instances making them more likely to be selected to the training subset of the next iteration.…”

Section: The Learn++ Algorithmmentioning

confidence: 99%

Ensemble Based Data Fusion for Early Diagnosis of Alzheimer's Disease

Parikh

Stepenosky

Topalis

et al. 2005

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference

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We describe an ensemble of classifiers based data fusion approach to combine information from two sources, believed to contain complimentary information, for early diagnosis of Alzheimer's disease. Specifically, we use the event related potentials recorded from the Pz and Cz electrodes of the EEG, which are further analyzed using multiresolution wavelet analysis. The proposed data fusion approach includes generating multiple classifiers trained with strategically selected subsets of the training data from each source, which are then combined through a weighted majority voting. Several factors set this study apart from similar prior efforts: we use a larger cohort, specifically target early diagnosis of the disease, use an ensemble based approach rather then a single classifier, and most importantly, we combine information from multiple sources, rather then using a single modality. We present promising results obtained from the first 35 (of 80) patients whose data are analyzed thus far.

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Power frequency and wavelet characteristics in differentiating between normal and Alzheimer EEG

Cited by 7 publications

References 9 publications

Quantile graphs for EEG-based diagnosis of Alzheimer’s disease

Quantile graphs for EEG-based diagnosis of Alzheimer’s disease

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

Ensemble Based Data Fusion for Early Diagnosis of Alzheimer's Disease

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