Computational methods of EEG signals analysis for Alzheimer’s disease classification

Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for A...

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Bibliographic Details
Published in:Scientific reports Vol. 13; no. 1; pp. 8184 - 14
Main Authors: Vicchietti, Mário L., Ramos, Fernando M., Betting, Luiz E., Campanharo, Andriana S. L. O.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 20.05.2023
Nature Publishing Group
Nature Portfolio
Subjects:
631/378/1689/1283
639/166/985
639/766/530/2801
Aged
Algorithms
Alzheimer Disease - diagnosis
Alzheimer's disease
Biomarkers
Brain research
Cognitive ability
Cognitive Dysfunction - diagnosis
Computational neuroscience
Dementia
EEG
Electroencephalography
Electroencephalography - methods
Entropy
Fourier transforms
Humanities and Social Sciences
Humans
multidisciplinary
Neurodegeneration
Neurodegenerative diseases
Quality of Life
Science
Science (multidisciplinary)
Time series
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-32664-8