UKF-based model parameter estimation to localize the seizure onset zone in ECoG.

Journal: IEEE journal of biomedical and health informatics
Published Date: May 21, 2025

Abstract

Drug-resistant epilepsy (DRE) patients typically require surgical intervention or neurostimulation. Therefore, accurate localization of the seizure onset zone (SOZ) is essential for effective clinical intervention. Although some physiologically meaningful parameters of neural computational models show substantial differences across brain regions during seizures, few studies pay attention to applying these model parameters to SOZ localization. To investigate whether the parameter can be used for accurate SOZ localization, the unscented kalman filter (UKF) is employed to estimate the excitatory-inhibitory balance parameter c from the Z6 neural computational model using DRE patients' electrocorticography (ECoG). The results indicate that this parameter follows a unimodal distribution during the pre-ictal period and the post-ictal period, while exhibiting a bimodal distribution during the ictal period. Then, the distribution of this parameter is combined with machine learning methods, and a bagged tree classifier is constructed to localize the SOZ. The classification results demonstrate that the classifier based on parameter distributions exhibits excellent performance, particularly during the post-ictal period, with an average accuracy of 91.60%. Interestingly, SOZ localization is more accurate when no lesions are detected on magnetic resonance imaging (MRI) compared to when lesions are present. Finally, the model parameter distributions of the SOZs are utilized to predict the outcome of epilepsy surgery. Of note, the results demonstrate that the parameter distribution accurately predicts surgical outcomes with an average accuracy of 92.56%. These findings suggest that the distribution of neural computational model parameters may serve as biomarkers for SOZ localization and epilepsy surgery outcome prediction, providing valuable support and assistance for clinical decision-making.

Authors

  • Junfeng Lu
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
  • Donghui Zhang
    Department of Pathology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, 510095, China.
  • Kunlin Guo
  • Kunying Meng
  • Denghai Wang
    Gaotai Tianhong Biochemical Technology Development Co. Ltd., Gaotai, Gansu, China.
  • Kai Lu
    College of Computer, National University of Defense Technology, China.
  • Renping Yu
    School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China and Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina 27599.
  • Lifang Yang
    School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China; Industrial Technology Research Institute, Zhengzhou University, Zhengzhou, Henan, China.
  • MengMeng Li
    Key Laboratory of Chinese Materia Medica, Ministry of Education of Heilongjiang University of Chinese Medicine, No. 24 Haping Road, Xiangfang District, Harbin, 150040, PR China.
  • Rui Zhang
    Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
  • Hong Wan
    School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China; Industrial Technology Research Institute, Zhengzhou University, Zhengzhou, Henan, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, Henan, China. Electronic address: wanhong@zzu.edu.cn.
  • Mingming Chen

Keywords

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