Data_Sheet_1_Identification of Major Psychiatric Disorders From Resting-State Electroencephalography Using a Machine Learning Approach.pdf

We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameters including power spectrum density (PSD) and functional connectivity (FC) at frequency bands was used to establish models for the binary classification between patients with each disorder and HCs. The support vector machine, random forest, and elastic net ML methods were applied, and prediction performances were compared. The elastic net model with IQ adjustment showed the highest accuracy. The best feature combinations and classification accuracies for discrimination between patients and HCs with adjusted IQ were as follows: schizophrenia = alpha PSD, 93.83%; trauma and stress-related disorders = beta FC, 91.21%; anxiety disorders = whole band PSD, 91.03%; mood disorders = theta FC, 89.26%; addictive disorders = theta PSD, 85.66%; and obsessive–compulsive disorder = gamma FC, 74.52%. Our findings suggest that ML in EEG may predict major psychiatric disorders and provide an objective index of psychiatric disorders.

本研究旨在开发机器学习（ML）分类器，以利用脑电图（EEG）检测并对比各类重性精神障碍。我们回顾性收集了945名受试者的病历资料、心理评估所得的智商（IQ）得分，以及静息态评估下的定量脑电图（QEEG）数据；其中包括850名重性精神障碍患者（涵盖6大类及9种具体精神障碍）与95名健康对照（HCs）。本研究结合功率谱密度（PSD）、频段功能连接（FC）等定量脑电图参数，针对每种精神障碍患者与健康对照的二分类任务构建模型。分别采用支持向量机、随机森林与弹性网这三种机器学习方法，并对比其预测性能。经智商调整后的弹性网模型表现出最高的分类准确率。针对经智商调整后的患者与健康对照的区分任务，最优特征组合及分类准确率如下：精神分裂症：α频段功率谱密度，准确率93.83%；创伤及应激相关障碍：β频段功能连接，准确率91.21%；焦虑障碍：全频段功率谱密度，准确率91.03%；心境障碍：θ频段功能连接，准确率89.26%；成瘾障碍：θ频段功率谱密度，准确率85.66%；强迫症：γ频段功能连接，准确率74.52%。本研究结果表明，基于脑电图的机器学习方法可用于预测重性精神障碍，为精神障碍的客观评估提供量化指标。