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

Journal: Cognitive neurodynamics
Published Date: Dec 1, 2025

Abstract

UNLABELLED: 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.

Authors

  • Ying Li
    School of Information Engineering, Chang'an University, Xi'an 710010, China.
  • Lidao Xu
    South China Normal University, Guangzhou, Guangdong Province China.
  • Yibo Zhao
    Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
  • Mingxian Meng
    The First Clinical Medical College of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan Province China.
  • Yanan Chen
    School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China.
  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
  • Beijia Cui
    Department of Neurology, People's Hospital of Henan University, Zhengzhou, Henan Province China.
  • Jin Liu
    School of Computer Science and Engineering, Central South University, Changsha, China.
  • Jiuyan Han
    Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province China.
  • Na Wang
    College of Architecture and Civil Engineering, Xi'an University of Science and Technology Xi'an 710054 Shaanxi China wangna811221@xust.edu.cn +86-29-82202335 +86-29-82203378.
  • Ting Zhao
    Department of Neurology, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Henan Province, China.
  • Lei Sun
    1Department of Biological Engineering, Utah State University, 4105 Old Main Hill, Logan, UT 84322-4105 USA.
  • Zhe Ren
    BGI-Shenzhen , Beishan Industrial Zone 11th Building, Yantian District, Shenzhen , Guangdong 518083 , China.
  • Xiong Han
    Department of Neurology, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Henan Province, China.

Keywords

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