【作者】 王奇；

【导师】 韩雄；

【作者基本信息】 郑州大学 ， 神经病学（专业学位）， 2024， 硕士

【摘要】 目的:癫痫是一种慢性脑部疾病,焦虑障碍(anxiety disorders,AD)是癫痫患者(patients with epilepsy,PWE)最常见的共患病之一。基于症状学的主观问卷是当前诊断癫痫患者是否合并焦虑障碍的主要方式。开发鉴别癫痫患者是否共患焦虑障碍的客观诊断方法可以在一定程度上帮助癫痫患者共病焦虑障碍的临床诊断和治疗。本研究旨在基于脑电的锁相值(phase locking value,PLV)和Lempel-Ziv复杂度(Lempel-Ziv Complexity,LZC)特征构建诊断模型以帮助识别癫痫患者是否共患焦虑障碍。方法:本研究共纳入131例癫痫患者(patients with epilepsy,PWE)的脑电图数据进行回顾性分析。根据汉密尔顿焦虑评定量表(Hamilton Rating Scale for Anxiety,HAM-A)将患者分为两组。焦虑症组(anxiety disorder,AD,n=61)和非焦虑症组(non-anxiety disorder,NAD,n=70)。采用支持向量机(Support Vector Machine,SVM)和 k-近邻(K-Nearest Neighbor,KNN)两种算法分别构建 PLVEEG、LZCEEG和PLVEEG+LZCEEG三种特征模型。最后,通过统计学分析和计算受试者工作特征曲线下面积(receiver operating characteristic curve,AUC)来评价模型的性能。结果:综合分析,基于 k-近邻(K-Nearest Neighbor,KNN)的 PLVEEG+LZCEEG特征模型的效能最佳。五折交叉验证评分后模型的准确率、精确率、召回率、F1-分数和受试者工作特征曲线下面积(area under receiver operating characteristic curve,AUC)分别为87.89%、82.27%、98.33%、88.95%和0.89。当模型效率最优时,共对应29个脑电图特征。对这些特征进行进一步分析,共有22个脑电图特征在两组患者间存在显著差异,其中50%为alpha(α)-频段特征。结论:PLVEEG+LZCEEG特征模型可帮助识别癫痫患者是否共患焦虑障碍。PLVEEG+LZCEEG特征模型中的alpha(α)-频段特征可能是帮助识别癫痫患者共患焦虑障碍的潜在生物标志物。更多还原

【Abstract】 Objective:Epilepsy is a chronic brain disease.Anxiety disorders(AD)are one of the most common comorbidities in patients with epilepsy(PWE).Subjective questionnaires based on symptomatology are the main way to diagnose anxiety disorders in patients with epilepsy.The development of objective diagnostic methods to identify whether epilepsy patients have anxiety disorders can help clinical diagnosis and treatment to some extent.This study aimed to identify AD in PWE by constructing a diagnostic model based on the phase locking value(PLV)and Lempel-Ziv Complexity(LZC)features of the electroencephalogram(EEG).Methods:EEG data from 131 patients with epilepsy(PWE)were enrolled in this study to retrospective analyse.Patients were divided into two groups,anxiety disorder(AD,n=61)and non-anxiety disorder(NAD,n=70)according to the Hamilton Rating Scale for Anxiety(HAM-A).Support vector machine(SVM)and K-Nearest-Neighbor(KNN)algorithms were used to construct three models-the PLVEEG,LZCEEG,and PLVEEG+LZCEEG feature models.Finally,statistical analyses and the area under the receiver operating characteristic curve(AUC)were performed to evaluate the model performance.Results:In conclusion,the efficiency of the KNN-based PLCEEG+LZCEEG feature model was the best.And the accuracy,precision,recall,F1-score,and AUC of the model after five-fold cross-validations scores were 87.89%,82.27%,98.33%,88.95%,and 0.89,respectively.When the model efficiency was optimal,29 EEG features were suggested.Further analysis of these features indicated 22 EEG features that were significantly different between the two groups,including 50%of features are alpha(α)-band.Conclusion:The PLVEEG+LZCEEG model features can help identify AD in PWE.The PLVEEG and LZCEEG characteristics of the α-band may further be explored as a potential biomarker for AD in PWE.更多还原