机器学习与脑电信号分析相结合的眩晕状态分类  

Classification of vertigo state based on machine learning and electroencephalogram signal analysis

作　　者：耿跃华[1] 石金祥 Geng Yuehua;Shi Jinxiang(Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability,Hebei University of Technology,Tianjin 300130,China)

机构地区：[1]河北工业大学,省部共建电工装备可靠性与智能化国家重点实验室,天津市300130

出　　处：《中国组织工程研究》2022年第29期4624-4631,共8页Chinese Journal of Tissue Engineering Research

基　　金：国家自然科学基金面上项目(51877067),项目参与人:耿跃华。

摘　　要：背景:脑电图是临床上检测及分析眩晕的一种常用手段,目前多采用单极或多级导联描记并分析脑电频率是否异常。但眩晕的脑电活动过程是异常复杂的,仅采用频率快慢分析的方法,很难对眩晕状态进行准确的分类和检测。目的:将机器学习与脑电信号分析相结合对眩晕状态进行分类,这对眩晕的诊断具有一定的研究意义和临床应用价值。方法:采用无创的前庭功能调节技术前庭电刺激制造可逆的眩晕状态,刺激电流强度为1,2,4倍皮肤感知阈值,被试在不同强度电流刺激后需填写眩晕残障量表,根据眩晕障碍量表评估结果将眩晕症状分为不同的等级,以此作为脑电分类有监督学习的数据标签。采集刺激后的脑电信号,通过小波变换提取脑电信号的小波能量以及小波熵的样本特征,利用多种机器学习分类模型对有无眩晕以及不同等级眩晕的样本特征进行分类。结果与结论:①通过对多种分类模型分类结果的对比发现:基于脑电信号小波变换特征的有监督学习分类可以实现是否眩晕和眩晕等级的二分类和多分类;②随机森林分类模型较逻辑回归模型、支持向量机模型、反向传播神经网络模型在眩晕检测的二分类以及多分类问题上表现出较高的准确率,其中二分类准确率最高可达82.5%,操作特性曲线面积为0.913,三分类准确率最高可达75.8%,操作特性曲线面积为0.927;③结果表明,随机森林模型在有无眩晕及眩晕等级的脑电特征分类问题上具有较高的准确率。该方法为眩晕症状的分类检测提供了一种可行性的补充方案,为眩晕症的诊断提供了一个新的思路。BACKGROUND:Electroencephalogram(EEG)is a common means to detect and analyze vertigo in clinic.Currently,unipolar or multistage lead tracing is mostly used to record and analyze whether the EEG frequency is abnormal.However,the EEG process of vertigo is extremely complex.It is difficult to accurately classify and detect the vertigo state only using a frequency speed analysis.OBJECTIVE:To classify the types of vertigo based on the combination of machine learning and EEG signal analysis,which has certain research significance and clinical application value for the diagnosis of vertigo.METHODS:The non-invasive vestibular function regulation technology for vestibular electrical stimulation was used to create a reversible vertigo state.The stimulation current intensity was 1,2,and 4 times that of the skin perception threshold.All subjects were required to fill in a dizziness handicap inventory after different intensity current stimulations.The vertigo symptoms were divided into different grades according to the evaluation results of the dizziness handicap inventory,which was used as the data label for supervised learning of EEG classification.The stimulated EEG signals were collected,the wavelet energy and wavelet entropy sample features of EEG signals were extracted by wavelet transform,and a variety of machine learning classification models were used to classify the features of samples with or without vertigo and with different levels of vertigo.RESULTS AND CONCLUSION:By comparing the classification results of various classification models,we found that the supervised learning classification based on the wavelet transform characteristics of EEG signals could realize the binary classification and multi-classification of vertigo and vertigo level.Compared with logistic regression model,support vector machine model,back propagation neural network model,and random forest classification model showed higher accuracy in the binary classification and multi-classification of vertigo detection.The accuracy of the binary classi

关 键 词：前庭电刺激 眩晕 脑电信号 小波变换 分类算法 

分 类 号：R459.9[医药卫生—治疗学] R318[医药卫生—临床医学]