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Showing 2 results for Depressive Disorder
Prevalence Of Depressive Disordrs In Tehran Resident Population (year 2000)
Kaviani H, Ahmadi Abhari As, Nazari H, Hormozi K,
Volume 60, Issue 5 (8-2002)
Abstract

Depression is a debilitating disease that every one is likely to experience over a short or long term period of his or her life.

Methods and Materials: This study aimed to examine the one - month prevalence of anxiety and depression in Tehranian resident population. 1070 men and women (age 20-65) were screened by Beck Depression Inventory (BDI). Then, those who scored above the cut - off point were psychiatrically interviewed. The interviewers were blind to the respondents' scores on BDI 5% of the total sample were also added to the list of those to be interviewed. Interviewers were the third year psychiatric residents at Roozbeh hospital. Tehran, especially trained for this research's purpose.

Results: The results showed women (BDI- 12.16) are more depressed than men (BDI- 8.47). Furthermore, men (%16.7) were less likely to have depession disorders than women (% 30.50).

Conclusion: We will discuss discrepancies between (the present results and the results from previous research by others).


Mohsen Sadat Shahabi , Ahmad Shalbaf ,
Volume 82, Issue 2 (5-2024)
Abstract
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.
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.

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