An Approach Toward Artificial Intelligence Alzheimer’S Disease Diagnosis Using Brain Signals

An Approach Toward Artificial Intelligence Alzheimer’S Disease Diagnosis Using Brain Signals Publisher

Sadeghzadeh SA¹ ; Fakhri E¹ ; Bahrami M² ; Bagheri E¹ ; Khamsehashari R³ ; Noroozian M⁴ ; Hajiyavand AM⁵

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1. Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, ST4 2DE, United Kingdom
2. Behavioral Sciences Research Center, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 8174533871, Iran
3. Quality and Usability, Technical University of Berlin, Berlin, 10623, Germany
4. Cognitive Neurology and Neuropsychiatry Division, Department of Psychiatry, Tehran University of Medical Sciences, Tehran, 1416634793, Iran
5. Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham, B15 2SQ, United Kingdom

Source: Diagnostics Published:2023

Abstract

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. © 2023 by the authors.

Style	Citing Format
MLA	Sadeghzadeh SA, et al.. "An Approach Toward Artificial Intelligence Alzheimer’S Disease Diagnosis Using Brain Signals." Diagnostics, vol. 13, no. 3, 2023, pp. -.
APA	Sadeghzadeh SA, Fakhri E, Bahrami M, Bagheri E, Khamsehashari R, Noroozian M, Hajiyavand AM (2023). An Approach Toward Artificial Intelligence Alzheimer’S Disease Diagnosis Using Brain Signals. Diagnostics, 13(3), -.
Chicago	Sadeghzadeh SA, Fakhri E, Bahrami M, Bagheri E, Khamsehashari R, Noroozian M, Hajiyavand AM. "An Approach Toward Artificial Intelligence Alzheimer’S Disease Diagnosis Using Brain Signals." Diagnostics 13, no. 3 (2023): -.
Harvard	Sadeghzadeh SA et al. (2023) 'An Approach Toward Artificial Intelligence Alzheimer’S Disease Diagnosis Using Brain Signals', Diagnostics, 13(3), pp. -.
Vancouver	Sadeghzadeh SA, Fakhri E, Bahrami M, Bagheri E, Khamsehashari R, Noroozian M, et al.. An Approach Toward Artificial Intelligence Alzheimer’S Disease Diagnosis Using Brain Signals. Diagnostics. 2023;13(3):-.
BibTex	@article{ author = {Sadeghzadeh SA and Fakhri E and Bahrami M and Bagheri E and Khamsehashari R and Noroozian M and Hajiyavand AM}, title = {An Approach Toward Artificial Intelligence Alzheimer’S Disease Diagnosis Using Brain Signals}, journal = {Diagnostics}, volume = {13}, number = {3}, pages = {-}, year = {2023} }
RIS	TY - JOUR AU - Sadeghzadeh SA AU - Fakhri E AU - Bahrami M AU - Bagheri E AU - Khamsehashari R AU - Noroozian M AU - Hajiyavand AM TI - An Approach Toward Artificial Intelligence Alzheimer’S Disease Diagnosis Using Brain Signals JO - Diagnostics VL - 13 IS - 3 SP - EP - PY - 2023 ER -

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Abstract