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Optimizing automated detection of high frequency oscillations using visual markings does not improve SOZ

Trisha Mendoza1, Casey L Trevino1, Daniel W Shrey2

  • 1Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA.

Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology
|June 6, 2024
PubMed
Summary

Optimizing automated high frequency oscillation (HFO) detection using visual data did not improve seizure onset zone (SOZ) localization accuracy. Patient-specific optimization may be necessary for better results.

Keywords:
Automatic DetectionHigh Frequency OscillationsParameter OptimizationRefractory EpilepsySeizure Onset Zone LocalizationVisual Detection

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Area of Science:

  • Neuroscience
  • Epileptology
  • Biomedical Engineering

Background:

  • High frequency oscillations (HFOs) are key biomarkers for identifying the seizure onset zone (SOZ).
  • Automated HFO detection offers efficiency, but optimal parameter selection for maximizing SOZ localization accuracy remains unclear.
  • Current methods lack consensus on optimizing automated HFO detectors.

Purpose of the Study:

  • To optimize an automated HFO detector using visually identified HFOs.
  • To evaluate the impact of this optimization on SOZ localization accuracy.
  • To explore potential improvements through patient-specific parameter tuning.

Main Methods:

  • Intracranial EEG data from 20 epilepsy patients were analyzed.
  • HFOs were detected using three methods: unoptimized automated, visual identification, and visually optimized automated detection.
  • SOZ localization accuracy was assessed for each detection method.

Main Results:

  • SOZ localization accuracy did not significantly differ across the three HFO detection methods.
  • Optimized detector settings varied considerably between patients, with no single configuration proving universally effective.
  • Exploratory analysis indicated that patient-specific settings could potentially enhance SOZ localization.

Conclusions:

  • Visual optimization of automated HFO detectors does not enhance SOZ localization accuracy.
  • Current visual marking of HFOs is labor-intensive.
  • Developing patient-specific automated detection parameters is crucial for improving SOZ localization.