Work

Enhanced major depressive disorder diagnosis: A hybrid deep learning framework utilizing EEG and fMRI

Year: 2025

Type: article

Abstract: Major Depressive Disorder (MDD) is a severe mental disorder that requires early detection, which poses a need to enhance diagnostic accuracy. Although Electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) have demonstrated the ability to diagnose MDD, their combined application has not been extensively investigated. This work presents a new two-stream deep learning model for MDD diagnosis with EEG and fMRI data acquired simultaneously. The proposed approach is to process EEG and fMRI data independently by two different Convolutional Neural Networks (CNNs) to obtain discriminative features simultaneously. These features were merged and then classified using a support vector machine optimized by a genetic algorithm. The proposed model achieved a mean diagnostic accuracy of 98.67%, along with a recall of 96.43% and a precision of 100%. This study demonstrates the framework’s effectiveness as a tool to assist clinicians in enhancing the accuracy of MDD diagnosis and facilitating timely treatment planning.

Source: Journal of King Saud University - Science

DOI: 10.25259/jksus_8_2024

Authors: Guojin Ma, Jiajing Li, Jungu Liu, Xiuyu Lin

Institutions: Changchun University of Chinese Medicine, The Affiliated Hospital to Changchun University of Chinese Medicine, Jilin Maternity and Child Health Care Hospital

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $1,600