Volume 82, Issue 2 (May 2024)
Tehran Univ Med J 2024, 82(2): 125-133 |
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Sadat Shahabi M, Shalbaf A. تشخیص اختلال افسردگی عمده با استفاده از روشهای هوش مصنوعی
مبتنی بر سیگنال الکتروانسفالوگرام. Tehran Univ Med J 2024; 82 (2) :125-133
URL: http://tumj.tums.ac.ir/article-1-13038-en.html
URL: http://tumj.tums.ac.ir/article-1-13038-en.html
1- Department of Physics and Biomedical Engineering, School of Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Abstract: (40 Views)
Background: Major Depressive Disorder (MDD) is one of the most prevalent and disabling mental disorders in the world. Due to the life quality decline caused by this disease and its growing nature, timely detection and treatment is of paramount importance. In the present study Electroencephalogram (EEG) signal utilized for the precise detection of MDD using Artificial Intelligence (AI) Methods.
Methods: In this analytic study, which is done in Shahid Beheshti University of medical Sciences in 2023, fifty eight subjects were investigated using an experienced psychiatrist that 30 subjects diagnosed as MDD and 28 determined to be healthy. Nineteen channels EEG signals in resting state with eyes closed situation acquired for five minutes from all of the participants including 36 men and 22 women with the average age of 39.3 years. The EEG signals were preprocessed to remove contaminating signals from brain-originated signals. The EEGLAB package in MATLAB utilized to re-reference channels to the average reference, apply a band-pass filter between 1 and 40 Hz and to remove non-brain components of the signal using Independent Component Analysis (ICA). The cleaned data segmented to the three seconds windows with 50 percent overlapping. These segments were used as the input to the AI models. Deep Learning (DL) models utilized in the present study were EEGNet, ShallowConvNet and DeepConvNet which were developed based on the deep convolutional models for the classification of healthy and MDD brain signals. The main difference between these models laid in the number of specific convolutional layers and the model complexity.
Methods: In this analytic study, which is done in Shahid Beheshti University of medical Sciences in 2023, fifty eight subjects were investigated using an experienced psychiatrist that 30 subjects diagnosed as MDD and 28 determined to be healthy. Nineteen channels EEG signals in resting state with eyes closed situation acquired for five minutes from all of the participants including 36 men and 22 women with the average age of 39.3 years. The EEG signals were preprocessed to remove contaminating signals from brain-originated signals. The EEGLAB package in MATLAB utilized to re-reference channels to the average reference, apply a band-pass filter between 1 and 40 Hz and to remove non-brain components of the signal using Independent Component Analysis (ICA). The cleaned data segmented to the three seconds windows with 50 percent overlapping. These segments were used as the input to the AI models. Deep Learning (DL) models utilized in the present study were EEGNet, ShallowConvNet and DeepConvNet which were developed based on the deep convolutional models for the classification of healthy and MDD brain signals. The main difference between these models laid in the number of specific convolutional layers and the model complexity.
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Results: MDD and Healthy signals classification has been done using EEGNet, ShallowConvNet and DeepConvNet models and accuracy of 92.3%, 83.2% and 92.2% were achieved, respectively. Also EEGNet acquired the highest sensitivity of 98.9% and specificity of 79.1%.
Conclusion: The detection of MDD patients using EEG signals with high accuracy and generalizability is possible and proposed AI models can be utilized in the clinical settings as assistant tools. |
Keywords: artificial intelligence, deep learning, electroencephalography, major depressive disorder.
Type of Study: Original Article |
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