Ammar Falih Mahdi
@ruc.edu.iq
Computer Science / E-Learning Unit
AL - Rafidain University College
EDUCATION
PHD in Computer Science - Artificial Intelligence and Web Programming, M.Sc. in Computer Science - Artificial Intelligence, B.Sc. in Computer Science.
RESEARCH INTERESTS
Artificial Intelligence, Web Application Programming, Mobile Programming
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Ammar Falih Mahdi and Aseel Khalid Ahmed
Institute of Advanced Engineering and Science
One of the most common causes of functional frailty is major depressive disorder (MDD). MDD is a chronic condition that requires long-term therapy and professional assistance. Additionally, MDD effective treatment requires early detection. Unfortunately, it has intricated clinical characteristics that make early diagnosis and treatment difficult for clinicians. Furthermore, there are currently no clinically effective diagnostic biomarkers that can confirm an MDD diagnosis. However, electroencephalogram (EEG) data from the brain have recently been used to make a quantitative diagnosis of MDD. In addition, As being among the most cutting-edge artificial intelligence (AI) technologies, deep learning (DL) has exhibited superior performance in a wide range of real-world applications, from computer vision to healthcare. However, an additional challenge could be the extraction of information from the ECG raw data. This paper presents a method for converting EEG data to power spectral density (PSD) images, and then they were classified as healthy or MDD using a deep neural network for feature extraction and a machine learning (ML) classifier. When employing the proposed approach, the images formed from the PSD show a considerably improved performance in classification results.