Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach.

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach. Clin EEG Neurosci. 2020 Jun 03;:1550059420916634 Authors: Uyulan C, Ergüzel TT, Unubol H, Cebi M, Sayar GH, Nezhadasad M, Tarhan N Abstract The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing ...
Source: Clinical EEG and Neuroscience - Category: Neuroscience Authors: Tags: Clin EEG Neurosci Source Type: research