Why the study?
The authors aimed to review literature on combining nonlinear analysis with machine learning methods for depression detection or prediction, focusing on affordable, data-driven approaches using EEG recordings.
Does nonlinear analysis combined with machine learning on EEG data improve the detection of depression in patients with MDD?
Does nonlinear analysis combined with machine learning on EEG data improve the detection of depression in patients with MDD?
Answers are grounded in this paper and cite their sources.
Nonlinear analysis of resting-state EEG combined with machine learning shows promise for accurate, data-driven detection of depression, though methodological heterogeneity remains a challenge.
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.
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A 2019 study studied this question.
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