Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimer's Disease

@article{Alnuaimi2018ComplexityMF,
  title={Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimer's Disease},
  author={Ali H. Al-nuaimi and Emmanuel Jammeh and Lingfen Sun and Emmanuel C. Ifeachor},
  journal={Complex.},
  year={2018},
  volume={2018},
  pages={8915079:1-8915079:12}
}
Alzheimer’s disease (AD) is a progressive disorder that affects cognitive brain functions and starts many years before its clinical manifestations. A biomarker that provides a quantitative measure of changes in the brain due to AD in the early stages would be useful for early diagnosis of AD, but this would involve dealing with large numbers of people because up to 50% of dementia sufferers do not receive formal diagnosis. Thus, there is a need for accurate, low-cost, and easy to use biomarkers… 
Electroencephalogram Based Biomarkers for Detection of Alzheimer’s Disease
  • Ali H. Al-nuaimi
  • Biology, Medicine
    Neuroimaging - Neurobiology, Multimodal and Network Applications
  • 2020
TLDR
Electroencephalogram-based biomarkers may be used as a first-line decisionsupport tool in AD diagnosis and could complement other AD biomarkers.
Electroencephalogram Based Biomarkers for Detection of Alzheimer’s Disease
TLDR
Electroencephalogram (EEG)-based biomarkers may be used as a first-line decision-support tool in AD diagnosis and could complement other AD biomarkers.
Combination of Tsallis Entropy and Higutchi Fractal Dimension for Quantifying Changes in EEG signals in Alzheimer’s Disease
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The present work proposes the combination of Tsallis Entropy and Higuchi Fractal Dimension, in a common framework for either the entire EEG or on each frequency separately, to examine the performance in Mild Cognitive Impairment (MCI) and AD subjects.
Robust EEG Based Biomarkers to Detect Alzheimer’s Disease
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This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers.
Quantile graphs for EEG-based diagnosis of Alzheimer’s disease
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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.
Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review
TLDR
The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD.
EEG evidence of compensatory mechanisms in preclinical Alzheimer's disease.
Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer's disease and to better understand the pathophysiological processes of disease progression. Preclinical
The Significance of EEG Alpha Oscillation Spectral Power and Beta Oscillation Phase Synchronization for Diagnosing Probable Alzheimer Disease
TLDR
The study suggests that α oscillation spectral power and β oscillation phase synchronization index correlated well with the MMSE/MoCA test scores in AD groups, and might be useful metrics for population screening of probable AD patients.
Complexity Analysis of EEG Signal in Patients with Cognitive Impairment Using the Hjorth Descriptor
  • S. Hadiyoso, T. Mengko, H. Zakaria
  • Computer Science
    2019 2nd International Conference on Bioinformatics, Biotechnology and Biomedical Engineering (BioMIC) - Bioinformatics and Biomedical Engineering
  • 2019
TLDR
This research proposes an approach based on complexity analysis for the differentiation of subjects with cognitive impairment and normal subjects as an attempt to early detection of Alzheimer’s.
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