Machine Learning Approaches for Diagnosing Depression Using EEG: A Review

Overview

Journal Transl Neurosci

Specialty Neurology

Date 2022 Sep 1

PMID 36045698

Authors

Yuan Liu

Changqin Pu

Shan Xia

Dingyu Deng

Xing Wang

Mengqian Li

Affiliations

Soon will be listed here.

Abstract

Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.

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