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An Approach toward Artificial Intelligence Alzheimer’s Disease Diagnosis Using Brain Signals

Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment...

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Autores principales: Sadegh-Zadeh, Seyed-Ali, Fakhri, Elham, Bahrami, Mahboobe, Bagheri, Elnaz, Khamsehashari, Razieh, Noroozian, Maryam, Hajiyavand, Amir M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913919/
https://www.ncbi.nlm.nih.gov/pubmed/36766582
http://dx.doi.org/10.3390/diagnostics13030477
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author Sadegh-Zadeh, Seyed-Ali
Fakhri, Elham
Bahrami, Mahboobe
Bagheri, Elnaz
Khamsehashari, Razieh
Noroozian, Maryam
Hajiyavand, Amir M.
author_facet Sadegh-Zadeh, Seyed-Ali
Fakhri, Elham
Bahrami, Mahboobe
Bagheri, Elnaz
Khamsehashari, Razieh
Noroozian, Maryam
Hajiyavand, Amir M.
author_sort Sadegh-Zadeh, Seyed-Ali
collection PubMed
description Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers.
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spelling pubmed-99139192023-02-11 An Approach toward Artificial Intelligence Alzheimer’s Disease Diagnosis Using Brain Signals Sadegh-Zadeh, Seyed-Ali Fakhri, Elham Bahrami, Mahboobe Bagheri, Elnaz Khamsehashari, Razieh Noroozian, Maryam Hajiyavand, Amir M. Diagnostics (Basel) Article Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers. MDPI 2023-01-28 /pmc/articles/PMC9913919/ /pubmed/36766582 http://dx.doi.org/10.3390/diagnostics13030477 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sadegh-Zadeh, Seyed-Ali
Fakhri, Elham
Bahrami, Mahboobe
Bagheri, Elnaz
Khamsehashari, Razieh
Noroozian, Maryam
Hajiyavand, Amir M.
An Approach toward Artificial Intelligence Alzheimer’s Disease Diagnosis Using Brain Signals
title An Approach toward Artificial Intelligence Alzheimer’s Disease Diagnosis Using Brain Signals
title_full An Approach toward Artificial Intelligence Alzheimer’s Disease Diagnosis Using Brain Signals
title_fullStr An Approach toward Artificial Intelligence Alzheimer’s Disease Diagnosis Using Brain Signals
title_full_unstemmed An Approach toward Artificial Intelligence Alzheimer’s Disease Diagnosis Using Brain Signals
title_short An Approach toward Artificial Intelligence Alzheimer’s Disease Diagnosis Using Brain Signals
title_sort approach toward artificial intelligence alzheimer’s disease diagnosis using brain signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913919/
https://www.ncbi.nlm.nih.gov/pubmed/36766582
http://dx.doi.org/10.3390/diagnostics13030477
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