Predicting antiseizure medication response in newly diagnosed epilepsy using quantitative EEG and machine learning.

Journal: Seizure
Published Date: May 13, 2025

Abstract

BACKGROUND: Predicting long-term outcomes in newly diagnosed epilepsy remains limited by reliance on clinical features and visual EEG interpretation. Machine learning enhances this potential by identifying complex patterns in EEG data, as demonstrated in studies on predicting surgical outcomes and seizure initiation. However, its application to predicting ASM response in newly diagnosed epilepsy has been underexplored. This study aimed to develop a machine learning model to predict ASM response in newly diagnosed epilepsy patients, with the goal of improving personalized treatment strategies and early identification of drug resistance.

Authors

  • Gha-Hyun Lee
    Department of Neurology, Pusan National University Hospital, Busan, South Korea; Pusan National University School of Medicine, Research Institute for Convergence of Biomedical Science and Technology, Yangsan, South Korea.
  • Sang Min Sung
    Stroke Center, Pusan National University Hospital, School of Medicine, Busan, Republic of Korea; Department of Neurology, Pusan National University Hospital, School of Medicine, Busan, Republic of Korea; Biomedical Research Institute, Pusan National University Hospital, School of Medicine, Busan, Republic of Korea. Electronic address: aminoff@hanmail.net.
  • Kwang-Dong Choi
    Department of Neurology, Pusan National University Hospital, Busan, South Korea; Pusan National University School of Medicine, Research Institute for Convergence of Biomedical Science and Technology, Yangsan, South Korea.
  • Jiyoung Kim
    Department of Material Sciences and Engineering, University of Texas at Dallas, Richardson, TX, 75080, USA.
  • Jae Wook Cho
    The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA.
  • Sang Ho Kim
    KSH Neuroclinic, Busan, South Korea.

Keywords

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