基于概率稀疏自注意力神经网络的重性抑郁疾患诊断  

Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network

作　　者：秦璟 秦志光[1] 李发礼 彭悦恒 QIN Jing;QIN Zhiguang;LI Fali;PENG Yueheng(School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu Sichuan 610054,China;School of Life Science and Technology,University of Electronic Science and Technology of China,Chengdu Sichuan 610054,China)

机构地区：[1]电子科技大学信息与软件工程学院,成都610054 [2]电子科技大学生命科学与技术学院,成都610054

出　　处：《计算机应用》2024年第9期2970-2974,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62027827)。

摘　　要：抑郁症的诊断主要依赖于医师的咨询和量表评估等主观方法,可能导致误诊。脑电图(EEG)具有高时间分辨率、低成本、易于设置和无创等优点,因此可以用作精神障碍(如抑郁症)的定量测量工具。深度学习算法目前在EEG信号上有多种应用,其中就包括抑郁症的诊断和分类。EGG信号在通过自注意力机制处理时有大量的冗余部分,因此,提出一种基于概率稀疏自注意力机制的卷积神经网络(PSANet)。首先,根据采样因数在自注意力机制中选取少量最关键的注意力点,在运用自注意力机制的同时克服它计算成本高的缺点,使它可以在脑电长序列数据上应用;同时将脑电图与患者的生理量表进行嵌合,从而进行多维度诊断。在一个包含抑郁症患者和健康对照组的数据集上进行实验评估,实验结果表明,PSANet表现出较高的分类准确性,参数量也低于EEGNet等对比方法。The diagnosis of major depressive disorder predominantly relies on subjective methods,including physician consultations and scale assessments,which may lead to misdiagnosis.EEG(ElectroEncephaloGraphy)offers advantages such as high temporal resolution,low cost,ease of setup,and non-invasiveness,making it a potential quantitative measurement tool for psychiatric disorders,including depressive disorder.Recently,deep learning algorithms have been diversely applied to EEG signals,notably in the diagnosis and classification of depressive disorder.Due to significant redundancy is observed when processing EEG signals through a self-attention mechanism,a convolutional neural network leveraging a Probabilistic sparse Self-Attention mechanism(PSANet)was proposed.Firstly,a limited number of pivotal attention points were chosen in the self-attention mechanism based on the sampling factor,addressing the high computational cost and facilitating its application to extensive EEG data sequences;concurrently,EEG data was amalgamated with patients’physiological scales for a comprehensive diagnosis.Experiments were executed on a dataset encompassing both depressive disorder patients and a healthy control group.Experimental results show that PSANet exhibits superior classification accuracy and a reduced number of parameters relative to alternative methodologies such as EEGNet.

关 键 词：抑郁症诊断 脑电图 深度学习 自注意力机制 卷积神经网络 

分 类 号：TP389.1[自动化与计算机技术—计算机系统结构]