情绪与抑郁症神经网络建模及微状态分析
Neural network modeling and microstate analysis of emotion and depression
黄信 1李跃忠 1李小俚 1边楠楠1
作者信息
- 1. 东华理工大学机械与电子工程学院 南昌 330013
- 折叠
摘要
重度抑郁症表现为心情低落、思维迟缓,而情绪是人对客观事物的态度体验以及相应人脑神经元的行为反应.已有文章对抑郁症、情绪分类的分类网络进行搭建,但网络功能单一,只能完成单一分类任务,且没有很好的将心理疾病与人体的情绪、语言表达、眨眼等行为结合.本研究探讨情绪分类和抑郁症诊断的指标特征相关性,并设计网络模型跨种类跨数据集验证通过情绪诊断抑郁症的可行性.提取微分熵作为网络结构输入特征,使用卷积神经网络研究SEED-IV的情绪分类和 MODMA情绪占比.并分析两种数据集的微状态参数,对具有相同微状态类型的样本进行分析并探索两者微状态之间的相关性.α与γ节律上的分类结果和微状态的相关系数差异能够较好进行情绪分类和抑郁症诊断.在验证了α与γ节律中均有参数呈现出情绪与抑郁症的相关性后,设计实验证明了通过增加微状态特征的方法可以捕捉到抑郁症患者异常大脑特性,可以在情绪识别CNN中添加有关联性的微状态参数完成抑郁症的诊断.
Abstract
Major depression is characterized by low mood and slow thinking,and emotion is the attitude experience of object and the corresponding behavioral response of human brain neurons.Many articles have designed classification network of depression and emotion classification,but the network function is single,can only complete a single classification task,and does not combine mental illness with human emotions,language expression,blinking and other behaviors well.The article explores the correlation of index characteristics between emotion classification and depression diagnosis,then designs a network to verify the feasibility of diagnosing depression through emotion across categories and datasets.The differential entropy is extracted as the input feature of the network,and the convolutional neural network is used to study the emotion classification of SEED-IV and the MODMA emotion proportion.Analyze the microstate parameters of the two datasets,samples with the same microstate type are analyzed and the correlation between the two microstate is explored.The difference of α and γ rhythm classification results and microstate correlation coefficients can be used to classify emotions and diagnose depression.After verifying that parameters in both α and γ rhythms show the correlation between emotion and depression,the design experiment proves that abnormal brain characteristics of patients with depression can be captured by adding microstate features,and the diagnosis of depression can be completed by adding correlated microstate parameters to CNN used for emotion recognition.
关键词
深度学习/微状态/情绪分类/抑郁症/跨数据集诊断Key words
deep learning/microstate/emotion classification/depression/diagnose across datasets引用本文复制引用
出版年
2024
NETL
NSTL
万方数据