International Conference on Computer and Knowledge Engineering

Home / 14th International Conference on Computer and Knowledge Engineering

Depression Diagnosis Using Optimization of Nonlinear EEG Features Based on Parametric Learning Tactics

Authors :

Ali Asadi Zeidabadi¹ Melika Changizi² Mahdi Zolfagharzadeh Kermani³ Sara Bargi Barkouk⁴

1- Medical Sciences & Technologies Faculty, Science & Research Branch Islamic Azad University Tehran, Iran 2- Medical Sciences & Technologies Faculty, Science & Research Branch Islamic Azad University Tehran, Iran 3- Medical Sciences & Technologies Faculty, Science & Research Branch Islamic Azad University Tehran, Iran 4- Medical Sciences & Technologies Faculty, Science & Research Branch Islamic Azad University Tehran, Iran

Keywords :

Depression،EEG،machine learning،Nonlinear Features،Parametric Learning

Abstract :

Major Depressive Disorder (MDD) presents substantial challenges in mental health due to its intricate and multifaceted nature. In this study, we propose a framework for diagnosing MDD using the optimization of nonlinear features extracted from electroencephalogram (EEG) signals, utilizing a parametric learning tactic. Recognizing the inherent chaotic and nonlinear properties of EEG signals, we implemented a method involving dual windowing tactics and temporal overlap for feature extraction. This methodology enabled the identification of significant nonlinear features that effectively distinguish between MDD patients and healthy controls. Using the MUMTAZ dataset, we conducted rigorous feature selection and classification, employing K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers. Our results indicate that the KNN classifier achieved the highest accuracy of 98.28%, demonstrating the efficacy of parametric learning tactics in optimizing nonlinear EEG feature extraction for accurate MDD diagnosis.

Papers List

List of archived papers

Leveraging Self-Supervised Models for Automatic Whispered Speech Recognition

Aref Farhadipour - Homa Asadi - Volker Dellwo

Security Analysis of MiniApps: Vulnerabilities, Exploits, and a Tailored Mitigation Framework

Keyhan Mohammadi - Arman Moradi - Reza Ebrahimi Atani

LightFedSelect: A Lightweight Framework for Byzantine-Robust Federated Learning

Seyed Saeed Razavi - Seyed Arsalan Vasegh Rahim Parvar - Soroosh Dadashi Pakdeh - Mohammad Matin Rezaeifard - Morteza Mollaie Chafi - Reza Ebrahimi Atani

Analysis of Address Lifespans in Bitcoin and Ethereum

Amir Mohammad Karimi Mamaghan - Amin Setayesh - Behnam Bahrak

Systematic review on AI techniques in detection and navigation of agricultural machines and robots

Afsaneh Soleimani - Mohammad Boghrati - Hossein Damavandi

An intelligent linguistic error detection approach to automated diagnosis of Dyslexia disorder in Persian speaking children

Fatemeh Asghari - Mahsa Khorasani - Mohsen Kahani - Seyed Amir Amin Yazdi - Mahdi Arkhodi Ghalenoei

Reversible Data Insertion in Encryption Domain Based on Reduced Quad Difference Expansion

Alireza Ghaemi - Mohammad Zare Ehteshami - Amirhossein Ghaemi

Impact of Oversampling Methods on Imbalanced Dataset for Software Fault Prediction

Alireza Abiri - Alireza Tajary - Mansoor Fateh

SUBoost: A Novel Boosting-Based Selective Undersampling for handling Imbalanced Data

Nima Rasi Baghmishe - Jafar Tanha - Ehsan Roshan

A Federated Learning-Based Hybrid Deep Learning Framework for Enhanced Human Activity Recognition

Jamileh Azmoudeh - Sajjad Arghaee - Parisa Valizadeh - Samaneh Dandani - Iman Havangi - Mohammad Hossein Yaghmaee