The class imbalance problem in automatic localization of the epileptogenic zone for epilepsy surgery: a systematic review.

Journal: Journal of neural engineering
Published Date:

Abstract

Accurate localization of the epileptogenic zone (EZ) is crucial for epilepsy surgery, but the class imbalance of epileptogenic vs. non-epileptogenic electrode contacts in intracranial electroencephalography (iEEG) data poses significant challenges for automatic localization methods. This review evaluates methodologies for handling the class imbalance in EZ localization studies that use machine learning (ML).We systematically reviewed studies employing ML to localize the EZ from iEEG data, focusing on strategies for addressing class imbalance in data handling, algorithm design, and evaluation.Out of 2,128 screened studies, 35 fulfilled the inclusion criteria. Across the studies, the iEEG contacts annotated as epileptogenic prior to automatic localization constituted a median of 18.34% of all contacts. However, many of these studies did not adequately address the class imbalance problem. Techniques such as data resampling and cost-sensitive learning were used to mitigate the class imbalance problem, but the chosen evaluation metrics often failed to account for it.Class imbalance significantly impacts the reliability of EZ localization models. More comprehensive management and innovative approaches are needed to enhance the robustness and clinical utility of these models. Addressing class imbalance in ML models for EZ localization will improve both the predictive performance and reliability of these models.

Authors

  • Valentina Hrtonova
    First Department of Neurology, Faculty of Medicine, Masaryk University, Pekarska 53, 602 00 Brno, Czech Republic; Institute of Scientific Instruments of the CAS, v. v. i., Kralovopolska 147, 612 00 Brno, Czech Republic; Department of Neurology, Duke University School of Medicine, 2424 Erwin Rd, Durham, NC 27705, the United States of America.
  • Kassem Jaber
    Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center, Durham, NC, United States of America.
  • Petr Nejedly
    Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.
  • Elizabeth R Blackwood
    Duke University Medical Center Library & Archives, Durham, NC, USA.
  • Petr Klimes
  • Birgit Frauscher
    Montreal Neurological Hospital, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Quebec, Canada; Department of Neurology, Duke University Medical School and Department of Biomedical Engineering, Pratt School of Engineering, 2424 Erwin Road, Durham, NC, 27705, USA. Electronic address: birgit.frauscher@duke.edu.