标题： 基于静息态脑电图（EEG）检测抑郁症的机器学习方法：一项综述研究 显示英文标题

标题： Machine learning approaches in Detecting the Depression from Resting-state Electroencephalogram (EEG): A Review Study

摘要： 本文旨在回顾目前在精神卫生保健领域中用于提高诊断和治疗管理准确性的一些不同方法。 我们的重点是情绪障碍，特别是重性抑郁障碍（MDD）。 我们首先回顾并讨论基于神经影像学研究（MRI 和 fMRI）的发现，以了解关于抑郁症解剖和功能差异的知识体系。 然后，我们将关注更经济的数据驱动方法，适用于日常临床实践，尤其是那些基于脑电图（EEG）记录的方法。 在这些利用 EEG 的研究中，我们讨论了一组用于检测静息态 EEG 基础上的抑郁症（检测研究）以及干预性研究（在方案中使用刺激或旨在预测治疗结果）的应用。 最后，我们讨论并回顾了提高所开发模型可靠性的指南，这些模型可能有助于改善精神病学中抑郁症的诊断。 显示英文摘要

摘要： In this paper, we aimed at reviewing several different approaches present today in the search for more accurate diagnostic and treatment management in mental healthcare. Our focus is on mood disorders, and in particular on the major depressive disorder (MDD). We are reviewing and discussing findings based on neuroimaging studies (MRI and fMRI) first to get the impression of the body of knowledge about the anatomical and functional differences in depression. Then, we are focusing on less expensive data-driven approach, applicable for everyday clinical practice, in particular, those based on electroencephalographic (EEG) recordings. Among those studies utilizing EEG, we are discussing a group of applications used for detecting of 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 improvement of diagnostic of depression in psychiatry.