Machine Learning Approaches for Detecting the Depression from Resting-State Electroencephalogram (EEG): A Review Study

2019/09/06 by Milena Čukić Radenković, Radenković, Milena Čukić, Victoria Lopez Lopez +1 · 1 citation
Computer Science · Engineering · Mathematics · Medicine · Neuroscience · Physics and Astronomy · #Chaotic Dynamics (nlin.CD) #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Functional Brain Connectivity Studies #Heart Rate Variability and Autonomic Control #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Signal Processing (eess.SP) #cs.LG #eess.SP #electronic engineering #information engineering #nlin.CD #stat.ML

paper · pdf · doi:10.48550/arxiv.1909.03115

30 pages, 1 table. arXiv admin note: substantial text overlap with arXiv1903.11454

openalex created_date 2019/04/01 · arxiv created 2019/09/06 · openalex publication_date 2019/09/06 · arxiv updated 2019/09/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we aimed at reviewing present literature on employing nonlinear analysis in combination with machine learning methods, in depression detection or prediction task. We are focusing on an affordable data-driven approach, applicable for everyday clinical practice, and in particular, those based on electroencephalographic (EEG) recordings. Among those studies utilizing EEG, we are discussing a group of applications used for detecting the depression based on the resting state EEG (detection studies) and interventional studies (using stimulus in their protocols or aiming to predict the outcome of therapy). We conclude with a discussion and review of guidelines to improve the reliability of developed models that could serve the improvement of diagnostic and more accurate treatment of depression.

Citations

• Cybernetics or Control and Communication in the Animal and the Machine
• A simulation study of the number of events per variable in logistic regression analysis
• Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal
• Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression
• Acute and Longer-Term Outcomes in Depressed Outpatients Requiring One or Several Treatment Steps: A STAR*D Report

Cited by

• Godot is not coming: when we will let innovations enter psychiatry?

Related

• Machine learning approaches in Detecting the Depression from Resting-state Electroencephalogram (EEG): A Review Study
• A Systematic Review of EEG-based Machine Intelligence Algorithms for Depression Diagnosis, and Monitoring
• Comparing linear, nonlinear and time frequency-based features in the EEMD domain for depression detection application using EEG signals
• Computer-Aided Diagnosis of Depression Using EEG Signals
• Machine learning with electroencephalography features for precise diagnosis of depression subtypes