Topographic differences in EEG microstates: distinguishing juvenile myoclonic epilepsy from frontal lobe epilepsy.

IF 3.1 3区工程技术 Q2 NEUROSCIENCES

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-05-10 DOI:10.1007/s11571-025-10255-9

Ying Li, Lidao Xu, Yibo Zhao, Mingxian Meng, Yanan Chen, Bin Wang, Beijia Cui, Jin Liu, Jiuyan Han, Na Wang, Ting Zhao, Lei Sun, Zhe Ren, Xiong Han

{"title":"Topographic differences in EEG microstates: distinguishing juvenile myoclonic epilepsy from frontal lobe epilepsy.","authors":"Ying Li, Lidao Xu, Yibo Zhao, Mingxian Meng, Yanan Chen, Bin Wang, Beijia Cui, Jin Liu, Jiuyan Han, Na Wang, Ting Zhao, Lei Sun, Zhe Ren, Xiong Han","doi":"10.1007/s11571-025-10255-9","DOIUrl":null,"url":null,"abstract":"This study aims to develop an exploratory classification model for Juvenile Myoclonic Epilepsy (JME) based on electroencephalogram (EEG) microstate features to assist clinical diagnosis and reduce misdiagnosis rates. A total of 123 participants were included in this study, consisting of 74 patients diagnosed with JME and 49 patients with Frontal Lobe Epilepsy (FLE). Resting-state EEG data were retrospectively collected from all participants. After preprocessing, microstate analysis was performed, and 24 microstate features (including duration, occurrence rate, coverage, and transition probability) were extracted and analyzed. Finally, the extracted microstate parameters were used to train six machine learning classifiers to distinguish between the two types of epilepsy. The performance of these models was assessed by calculating accuracy, precision, recall, F1 score, and area under the curve (AUC). The study found that all parameters of microstate A showed high consistency between the two groups. However, the JME group exhibited lower occurrence and smaller coverage of microstate B compared to the FLE group, while showing longer durations for microstate C. Additionally, the transition probabilities from microstate B to C and D were lower in the JME group, while the transition probability from C to D was significantly higher. When EEG microstate features were integrated into the six machine learning classifiers, the linear discriminant analysis (LDA) algorithm achieved the best classification performance (accuracy of 76.4%, precision of 79.5%, and AUC of 0.817). This study found significant differences in EEG microstate characteristics between JME and FLE. Based on 24 microstate features, a classification model was successfully developed and validated. These findings underscore the potential of EEG microstates as neurophysiological biomarkers for distinguishing between these two epilepsy types.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10255-9.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"72"},"PeriodicalIF":3.1000,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065695/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Neurodynamics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11571-025-10255-9","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/5/10 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

This study aims to develop an exploratory classification model for Juvenile Myoclonic Epilepsy (JME) based on electroencephalogram (EEG) microstate features to assist clinical diagnosis and reduce misdiagnosis rates. A total of 123 participants were included in this study, consisting of 74 patients diagnosed with JME and 49 patients with Frontal Lobe Epilepsy (FLE). Resting-state EEG data were retrospectively collected from all participants. After preprocessing, microstate analysis was performed, and 24 microstate features (including duration, occurrence rate, coverage, and transition probability) were extracted and analyzed. Finally, the extracted microstate parameters were used to train six machine learning classifiers to distinguish between the two types of epilepsy. The performance of these models was assessed by calculating accuracy, precision, recall, F1 score, and area under the curve (AUC). The study found that all parameters of microstate A showed high consistency between the two groups. However, the JME group exhibited lower occurrence and smaller coverage of microstate B compared to the FLE group, while showing longer durations for microstate C. Additionally, the transition probabilities from microstate B to C and D were lower in the JME group, while the transition probability from C to D was significantly higher. When EEG microstate features were integrated into the six machine learning classifiers, the linear discriminant analysis (LDA) algorithm achieved the best classification performance (accuracy of 76.4%, precision of 79.5%, and AUC of 0.817). This study found significant differences in EEG microstate characteristics between JME and FLE. Based on 24 microstate features, a classification model was successfully developed and validated. These findings underscore the potential of EEG microstates as neurophysiological biomarkers for distinguishing between these two epilepsy types.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10255-9.

查看原文本刊更多论文

脑电微观状态的地形差异：区分青少年肌阵挛性癫痫与额叶癫痫。

本研究旨在建立基于脑电图（EEG）微状态特征的青少年肌阵挛性癫痫（JME）探索性分类模型，以辅助临床诊断，降低误诊率。本研究共纳入123名参与者，包括74名JME患者和49名额叶癫痫（FLE）患者。回顾性收集所有参与者的静息状态脑电图数据。预处理后进行微状态分析，提取并分析24个微状态特征（包括持续时间、发生率、覆盖率、转移概率等）。最后，将提取的微状态参数用于训练6个机器学习分类器来区分两种类型的癫痫。通过计算准确率、精密度、召回率、F1分数和曲线下面积（AUC）来评估这些模型的性能。研究发现，微态A的所有参数在两组之间表现出高度的一致性。然而，与FLE组相比，JME组微状态B的发生率和覆盖率较低，而微状态C的持续时间较长。此外，JME组从微状态B到C和D的过渡概率较低，而从C到D的过渡概率明显较高。将EEG微状态特征整合到6种机器学习分类器中，线性判别分析（LDA）算法的分类性能最好，准确率为76.4%，精密度为79.5%，AUC为0.817。本研究发现JME和FLE在脑电图微状态特征上存在显著差异。基于24个微状态特征，成功建立并验证了分类模型。这些发现强调了EEG微状态作为区分这两种癫痫类型的神经生理生物标志物的潜力。补充信息：在线版本包含补充资料，下载地址为10.1007/s11571-025-10255-9。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Cognitive Neurodynamics 医学-神经科学

CiteScore

6.90

自引率

18.90%

发文量

140

审稿时长

12 months

期刊介绍： Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.