scholarly journals An analysis for Alzheimer’s disease using cross-correlation and averaged frequency of EEG data

2018 ◽  
Author(s):  
Yuri Watanabe ◽  
Yohei Kobayashi ◽  
Mieko Tanaka ◽  
Takashi Asada ◽  
Kenji Ishii ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cross Correlation ◽  
Eeg Data

scholarly journals Analysis for Alzheimer's disease using cross-correlation of EEG data

2017 ◽  
Author(s):  
Yuri Watanabe ◽  
Yohei Kobayashi ◽  
Mieko Tanaka ◽  
Takashi Asada ◽  
Kenji Ishii ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cross Correlation ◽  
Eeg Data

scholarly journals Direction of Information Flow in Alzheimer's Disease and MCI Patients

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Fabrizio Vecchio ◽  
Claudio Babiloni
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Information Flow ◽  
Additive Model ◽  
Functional Coupling ◽  
Directed Transfer Function ◽  
Directional Flow ◽  
Eeg Data ◽  
Rest Condition ◽  
Amnesic Mild Cognitive Impairment

Is directionality of electroencephalographic (EEG) synchronization abnormal in amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD)? And, do cerebrovascular and AD lesions represent additive factors in the development of MCI as a putative preclinical stage of AD? Here we reported two studies that tested these hypotheses. EEG data were recorded in normal elderly (Nold), amnesic MCI, and mild AD subjects at rest condition (closed eyes). Direction of information flow within EEG electrode pairs was performed by directed transfer function (DTF) atδ(2–4 Hz),θ(4–8 Hz),α1 (8–10 Hz),α2 (10–12 Hz),β1 (13–20 Hz),β2 (20–30 Hz), andγ(30–40 Hz). Parieto-to-frontal direction was stronger in Nold than in MCI and/or AD subjects forαandβrhythms. In contrast, the directional flow within interhemispheric EEG functional coupling did not discriminate among the groups. More interestingly, this coupling was higher atθ,α1,α2, andβ1 in MCI with higher than in MCI with lower vascular load. These results suggest that directionality of parieto-to-frontal EEG synchronization is abnormal not only in AD but also in amnesic MCI, supporting the additive model according to which MCI state would result from the combination of cerebrovascular and neurodegenerative lesions.

Discrimination of Alzheimer's disease and normal aging by EEG data

1997 ◽  
Vol 103 (2) ◽  
pp. 241-248 ◽  
Author(s):  
C. Besthorn ◽  
R. Zerfass ◽  
C. Geiger-Kabisch ◽  
H. Sattel ◽  
S. Daniel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Normal Aging ◽  
Eeg Data

The Value of Quantitative EEG Measures in the Early Diagnosis of Alzheimer’s Disease

2011 ◽  
pp. 132-140
Author(s):  
Hideaki Tanaka
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Quantitative Eeg ◽  
Frequency Resolution ◽  
Parahippocampal Gyrus ◽  
Data Sets ◽  
Electromagnetic Tomography ◽  
Eyes Closed ◽  
Eeg Data ◽  
Z Value

There is growing interest in the discovery of clinically useful, robust biomarkers for Alzheimer’s disease (AD) and pre-AD; the ability to accurately diagnose AD or to predict conversion from a preclinical state to AD would aid in both prevention and early intervention. This study aimed to evaluate the usefulness of a statistical assessment of cortical activity using electroencephalograms (EEGs) with normative data and the ability of such an assessment to contribute to the diagnosis of AD. 15 patients with AD and 8 patients with mild cognitive impairment (MCI) were studied. Eyes-closed resting EEGs were digitally recorded at 200 Hz from 20 electrodes placed according to the international 10/20 system on the scalp, and 20 artifact-free EEG epochs lasting 2.56 ms were selected. Each EEG epoch was down-sampled to 100 Hz and matched to the normal data sets. The selected EEGs from each subject were analyzed by standardized Low Resolution Electromagnetic Tomography (sLORETA) and statistically compared with the age-matched normal data sets at all frequencies. This procedure resulted in cortical z values for each EEG frequency with 0.39 Hz frequency resolution for each subject. Some of the AD and MCI patients presented a peak of negative z value around 20 Hz, revealing hypoactivity of the parahippocampal gyrus and the insula in the sLORETA cortical image. In addition, severe cases of AD showed decreased parietal activation. These results were in agreement with evidence from statistical neuroimaging using MRI/SPECT. Submission of normal EEG data sets to sLORETA might be useful for the detection of diagnostic and predictive markers of AD and MCI in individual patients.

scholarly journals EEG Characterization of the Alzheimer’s Disease Continuum by Means of Multiscale Entropies

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 544 ◽  
Author(s):  
Aarón Maturana-Candelas ◽  
Carlos Gómez ◽  
Jesús Poza ◽  
Nadia Pinto ◽  
Roberto Hornero
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Brain Activity ◽  
P Values ◽  
Eeg Data ◽  
High Prevalence ◽  
Average Slope ◽  
The Brain

Alzheimer’s disease (AD) is a neurodegenerative disorder with high prevalence, known for its highly disabling symptoms. The aim of this study was to characterize the alterations in the irregularity and the complexity of the brain activity along the AD continuum. Both irregularity and complexity can be studied applying entropy-based measures throughout multiple temporal scales. In this regard, multiscale sample entropy (MSE) and refined multiscale spectral entropy (rMSSE) were calculated from electroencephalographic (EEG) data. Five minutes of resting-state EEG activity were recorded from 51 healthy controls, 51 mild cognitive impaired (MCI) subjects, 51 mild AD patients (ADMIL), 50 moderate AD patients (ADMOD), and 50 severe AD patients (ADSEV). Our results show statistically significant differences (p-values < 0.05, FDR-corrected Kruskal–Wallis test) between the five groups at each temporal scale. Additionally, average slope values and areas under MSE and rMSSE curves revealed significant changes in complexity mainly for controls vs. MCI, MCI vs. ADMIL and ADMOD vs. ADSEV comparisons (p-values < 0.05, FDR-corrected Mann–Whitney U-test). These findings indicate that MSE and rMSSE reflect the neuronal disturbances associated with the development of dementia, and may contribute to the development of new tools to track the AD progression.

scholarly journals A Kernel-based Nonlinear Manifold Learning for EEG Channel Selection with Application to Alzheimer's Disease

2021 ◽  
Author(s):  
Shenal Rajintha Gunawardena ◽  
Ptolemaios G Sarrigiannis ◽  
Daniel J Blackburn ◽  
Fei He
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Manifold Learning ◽  
Latent Variable ◽  
Channel Selection ◽  
Dynamical Analysis ◽  
Support Vector ◽  
Variable Model ◽  
Eeg Data ◽  
Spatio Temporal

This paper introduces a novel EEG channel selection method to determine which channel interrelationships provide the best classification accuracy between a group of patients with Alzheimer's disease (AD) and a cohort of age matched healthy controls (HC). Thereby, determine which inter-relationships are more important for the in-depth dynamical analysis to further understand how neurodegenerative diseases such as AD affects global and local brain dynamics. The channel selection methodology uses kernel-based nonlinear manifold learning via Isomap and Gaussian Process Latent Variable Model (Isomap-GPLVM). The Isomap-GPLVM method is employed to learn both the spatial and temporal local similarities and global dissimilarities present within the EEG data structures. The resulting kernel (dis)similarity matrix is used as a measure of synchrony between EEG channels. Based on this information, channel-specific linear Support Vector Machine (SVM) classification is then used to determine which spatio-temporal channel inter-relationships are more important for in-depth dynamical analysis. In this work, the analysis of EEG data from HC and AD patients is presented as a case study. Our analysis shows that inter-relationships between channels in the fronto-parietal region and the rest are better at differentiating between AD and HC groups.

scholarly journals Combining complexity measures of EEG data: multiplying measures reveal previously hidden information

2015 ◽  
Author(s):  
Thomas F Burns
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complete Information ◽  
Eeg Signals ◽  
Hidden Information ◽  
Complexity Measures ◽  
Human Eeg ◽  
Unique Information ◽  
Eeg Data ◽  
Two Measures

Many studies have noted significant differences among human EEG results when participants or patients are exposed to different stimuli, undertaking different tasks, or being affected by conditions such as epilepsy or Alzheimer's disease. Such studies often use only one or two measures of complexity and do not regularly justify their choice of measure beyond the fact that it has been used in previous studies. If more measures were added to such studies, however, more complete information might be found about these reported differences. Such information might be useful in confirming the existence or extent of such differences, or in understanding their physiological bases. In this study I analysed publically-available EEG data using a range of complexity measures to determine how well the measures correlated with one another. The complexity measures did not all significantly correlate, suggesting that different measures were measuring unique features of the EEG signals and thus revealing information which other measures were unable to detect. Therefore, the results from this analysis suggests that combinations of complexity measures reveal unique information which is in addition to the information captured by other measures of complexity in EEG data. For this reason, researchers using individual complexity measures for EEG data should consider using combinations of measures to more completely account for any differences they observe and to ensure the robustness of any relationships identified.

scholarly journals Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
François-B. Vialatte ◽  
Justin Dauwels ◽  
Monique Maurice ◽  
Toshimitsu Musha ◽  
Andrzej Cichocki
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frequency Domain ◽  
Matched Control ◽  
Cost Effective ◽  
Eeg Signals ◽  
Time Frequency ◽  
Eyes Closed ◽  
Eeg Data ◽  
Rest Condition

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients.Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest wereθ(3.5–7.5 Hz),α1(7.5–9.5 Hz),α2(9.5–12.5 Hz), andβ(12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models.Results. Enhanced EEG power in theθrange is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies.Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

Sign in / Sign up

Export Citation Format

Share Document