Coherent false seizure prediction in epilepsy, coincidence or providence?

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Published Date: Nov 5, 2021

Abstract

OBJECTIVE: Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false and missing alarms of two algorithms on long-term datasets to show that the limitations are less related to classifiers or features, but rather to intrinsic changes in the data.

Authors

  • Jens Müller
    TU Dresden, Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, 01062 Dresden, Germany. Electronic address: jens.mueller1@tu-dresden.de.
  • Hongliu Yang
    TU Dresden, Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, 01062 Dresden, Germany.
  • Matthias Eberlein
    TU Dresden, Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, 01062 Dresden, Germany.
  • Georg Leonhardt
    TU Dresden, Neurosurgery of University Hospital Carl Gustav Carus, Fetscherstr. 74, 01307 Dresden, Germany.
  • Ortrud Uckermann
    TU Dresden, Neurosurgery of University Hospital Carl Gustav Carus, Fetscherstr. 74, 01307 Dresden, Germany.
  • Levin Kuhlmann
    4 Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria, 3010, Australia.
  • Ronald Tetzlaff
    TU Dresden, Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, 01062 Dresden, Germany.

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