سامانه همایش‌ها TSTA | ثبت‌نام و ارسال مقاله کنفرانس‌ها و کنگره‌های ملی

Home / دومین کنفرانس ملی عصر انفجار تکنولوژی؛ هوش مصنوعی، تحولی در صنعت، تجارت و زنجیره تامین و دومین کنفرانس ملی علم داده در کاربردهای مهندسی

Depression detection based on EEG signal analysis utilizing Inter-hemispheric Asymmetry and Correlation Dimension assessment

Authors :

Amirreza Ahmadi¹ Saeid Yarmohammdi² Ali Zeraatkar³ Reza Rostami⁴

1- دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران 2- دانشگاه آزاد اسلامی واحد تهران مرکزی 3- University of Victoria 4- دانشگاه تهران

Keywords :

EEG،Major Depressive Disorder،Machine Learning،Correlation Dimension،Inter-hemispheric Asymmetry

Abstract :

Depressive disorders represent the most significant health risk among mental illnesses. Diagnosing the disability in the first stages can improve treatment efficiency and save a patient’s life due to its curable characteristic. Questionnaire-based diagnostic criteria have been required for traditional depression diagnoses. This study suggests objective criteria and processed EEG signals of 17 MDD patients and 20 normal subjects to detect depression. The power of absolute and relative frequency bands and the inter-hemispheric asymmetry were extracted as the linear features, and the correlation dimension was considered as the non-linear feature. Five machine-learning models were used to classify the data. 91.7% of accuracy score was derived when the selected features with all the mentioned machine learning classifiers were used. In addition, the ROC-AUC score and F1 score were utilized for higher trustable results. The LR classifier demonstrated strong performance, achieving a peak F1 score of 93.3% (when using 'Absolute + Relative' features) and a peak ROC-AUC score of 97.1% (when using 'Relative' features). The results of the T-test have shown the Alpha inter-hemispheric asymmetry as not a robust biomarker. Besides, the correlation dimension was probed as an auxiliary biomarker in channels F8 and C4 to be applied with the other characteristics; the value of the T-test of other bands was insignificant. This study reveals the importance of feature selection and states that using the selected features and our suggested machine-learning models could provide a valuable tool for detecting depression.

Papers List

List of archived papers

Engineering pH-Responsive Hybrid Hydrogels via Inverse Suspension Polymerization for Novel Drug Delivery Systems

Borhan Oghbaei Bonab - Mohammad Ashraf - Sahar Enayati - Alireza Mahjub

Edge AI for Real-Time UAV Data Processing

Mohammad Mahdi Salmani

بررسی روش های تشخیص فیشینگ با استفاده از یادگیری ماشین

حامد منکرسی - غلامرضا احمدی

Fibroglandular Tissue Classification in Breast MRI: A Comparative Study of Automated Decision Strategies

Meysam Khalaj - Arvin Arian - Ala Torabi - Nasrin Ahmadinejad - Masoumeh Gity - Seyedeh Nooshin Miratashi Yazdi - Mohammad Pooya Afshari - Melika Sadeghi Tabrizi - Hamid Soltanian-Zadeh

ارائه مدلی برای کاربرد قابلیت های پویا در برندسازی هنر -صنعت فرش ‏دستبافت ایران: رویکردی آمیخته

پریسا عابدی - علی وندشعاری - حسین بوداقی خواجه نوبر - نسرین راضی

سامانه هوشمند پشتیبان تصمیم‌گیری راهبردی در تدارکات عمومی

حسن ضیافت

Biomechanical Evaluation and Comparison of Spinal Fixators in the Lumbar (L3–L4) Region Using the Finite Element Method

Nima Moazed - Mohammad Haghpanahi

تاثیر بعد استراتژی مالی وبعد پاسخگویی برکیفیت خدمات درک شده و خشنودی مشتریان )مورد مطالعه : فروشگاه افق کوروش(

حسین بوذری

استقرار حاکمیت شرکتی در هوش مصنوعی در جهت منافع عمومی

رعنا شهدآور - الهام رضا پور - وحید حسین زاده قویدل - آیسان صدقی

Impact of Dynamic and Static Sports on Growth and Anthropometric Characteristics (Height, Weight, BMI) in Children and Adolescents

Amin Partovi fard - Mahmoodreza Azghani - Sadra Jalali - Samin Asghari