JMIR Publications

ABSTRACT

Background:

Machine learning applications in healthcare have become numerous lately, and this work focuses on an important application in psychiatry related to the detection of depression. Since the advent of Computational Psychiatry, valuable research based on fMRI has had phenomenal results, but these tools tend to be simply too expensive for everyday clinical use.

Objective:

This article focuses on a much more affordable data-driven approach based on electroencephalographic (EEG) recordings. Further online applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from EEG recordings utilizing varying features and machine learning models.

Methods:

We have reviewed published studies based on resting-state EEG with final machine learning, used to detect depression (detecting studies), while also presenting a group of Interventional studies utilizing some form of stimulation in their method, aimed to predict therapy outcomes.

Results:

We have reviewed 14 studies (classified as detection studies) and 12 interventional studies published between 2008 and 2019. Since direct comparison was not possible due to the huge diversity of theoretical approaches and methods used, we compared them regarding the steps in analysis and accuracies yielded. We also compared possible drawbacks in terms of sample size but also in the process of feature extraction, feature selection, classification, internal and external validation as well as in possible unwarranted optimism and reproducibility regards. In addition we suggested desirable practices in avoiding misinterpretation of results and optimism.

Conclusions:

The work concludes with a discussion and review of guidelines to improve the reliability of developed models that may potentially improve diagnostics and offer more accurate treatment of depression in modern psychiatry. Clinical Trial: We did not perform a clinical trial but reviewed already published research. All those publications were previously having appropriate registration and ethics committee approval from their local Universities. Therefore we cannot report the trial registration number.