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:: Volume 11, Issue 3 (Summer 2023) ::
Shefaye Khatam 2023, 11(3): 68-80 Back to browse issues page
Alzheimer's Disease Diagnosis Using Brain Signals and Artificial Neural Networks
Hossein Khazaei , Elias Mazrooei Rad *
Department of Biomedical Engineering, Khavaran Institute of Higher Education, Mashhad, Iran , elias_mazrooei@yahoo.com
Abstract:   (535 Views)
Introduction: An unexpected number of people are at risk of Alzheimer's disease. Therefore, efforts to find effective preventive measures require to be intensified. Materials and Methods: To diagnose Alzheimer's disease through EEG signals using an artificial neural network, the first step involves pre-processing the recorded raw EEG data. This pre-processing includes the application of a 0.5 to 45 Hz bandpass filter to eliminate interference from the city's electrical signals. From the pre-processed data, the feature will be extracted. These features are related to time and frequency domains. Fourier transform, wavelet, first component analysis, nonlinear features of entropy, correlation dimension, and fractal dimension are among the suggested features. The extracted features will be evaluated by analysis of variance or t-test. The features that had the ability to separate different classes and have better statistical distribution in variance analysis or t-test are selected. Results: According to the capabilities of the artificial neural network in identifying different patterns and categorizing information that is set during a learning process, in this research, the artificial neural network will be used to determine the nonlinear mapping between EEG signals and the diagnosis of Alzheimer's disease. The database was divided into two categories: training and testing. In other words, the artificial neural network with the characteristics of the recorded signals as input and sick or healthy as the output of the neural network, and finally the the output of the trained artificial neural network is the diagnosis of sick or healthy data. In the final stage, the performance of the developed neural network will be evaluated and compared. Conclusion: Utilizing both EEG signals and artificial neural networks could represent a novel method for the diagnosis of Alzheimer's Disease in its early stages.
 
Keywords: Alzheimer Disease, Electroencephalography, Diagnosis
Full-Text [PDF 2059 kb]   (198 Downloads)    
Type of Study: Research --- Open Access, CC-BY-NC | Subject: Neurophysiology
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khazaei H, Mazrooei Rad E. Alzheimer's Disease Diagnosis Using Brain Signals and Artificial Neural Networks. Shefaye Khatam 2023; 11 (3) :68-80
URL: http://shefayekhatam.ir/article-1-2364-en.html


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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 11, Issue 3 (Summer 2023) Back to browse issues page
مجله علوم اعصاب شفای خاتم The Neuroscience Journal of Shefaye Khatam
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