一种基于EEG信号的抑郁症早期筛查方法  

Early Screening Method for Depression Based on EEG Signal

作　　者：任书瑶 宋江玲 张瑞[1] REN Shuyao;SONG Jiangling;ZHANG Rui(Medical Big Data Research Center,Northwest University,Xi’an 710127,China)

机构地区：[1]西北大学医学大数据研究中心,西安710127

出　　处：《计算机科学》2023年第S02期999-1004,共6页Computer Science

基　　金：国家自然科学基金(12071369,6200189);陕西省自然科学基金(2021JQ-430);陕西省重点研发计划(2019ZDLSF02-09-02,2017ZDXM-Y-095)。

摘　　要：抑郁症作为一类常见的、可治愈型的精神类疾病,若能在早期阶段对其进行有效筛查(即早期筛查)并及时采取相应的治疗手段,则可有效控制病情的进一步加重,甚至彻底治愈。传统的抑郁症诊断方法主要是医生通过患者的临床表现及临床检查(主要为诊断量表)进行综合判断,但诊断结果的准确与否严重依赖于医生的临床经验以及患者的高度配合。同时,由于抑郁症早期患者往往缺乏明显的病症表征,也极大增加了漏诊误诊的可能性。相关研究表明,脑电图(Electroencephalogram,EEG)能够反应受试者的精神状态,这为抑郁症的早期筛查提供了一种有效途径。基于此,以EEG信号为数据源,提出了一种基于EEG信号与深度学习的抑郁症早期筛查方法。首先,结合分段处理、频域转化等方法,对EEG信号进行时-频-空特征序列的提取;其次,基于所提特征序列与深度学习,构建了一种深度混合模型,通过训练模型完成正常人与轻度抑郁症患者的有效识别;最后,在公开数据集MODMA上验证所提方法的可行性与有效性。实验结果显示,早期筛查准确率为82.64%,召回率为78.42%,灵敏度为75.37%。Depression is a common and curable psychiatric disorder.If a prompt diagnosis can be taken at the early stage of depression(early screening),appropriate treatment could effectively control the depression progression or even cure it.The traditional method of diagnosing depression is a comprehensive judgment from doctors by clinical manifestations and clinical examination(diagnostic scales,etc.),but the diagnosis accuracy relies heavily on the clinical experience of the physician and the inclination of cooperation from the patient.In addition,early-stage symptoms of depression are difficult to observe,making traditional diagnostic methods susceptible to underdiagnosis.Research indicates that electroencephalogram(EEG)responds effectively to the mental state of subjects from a physiological perspective,which provides an effective way of early screening for depression.On this basis,this paper proposes an EEG-based method combined with deep learning models for early screening of depression.First,extracting the temporal-spectral-spatial sequences of EEG signals by segmentation processing,frequency domain transformation,etc.Secondly,constructing a hybrid deep neural network based on extracted sequences to identify the EEG signals of mild depression patients.Finally,the feasibility and effectiveness of proposed method are verified by conducting numerical experiments in the public datasets MODMA.Numerical results show that the accuracy,recall rate and sensitivity of the proposed method is 82.64%,78.42%,and 75.37%,respectively.

关 键 词：抑郁症 脑电信号 早期筛查 时-频-空特征序列 深度混合模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]