Localizing epileptogenic regions using high-frequency oscillations and machine learning

Shennan A. Weiss, Zachary Waldman, Federico Raimondo, Diego Slezak, Mustafa Donmez, Gregory Worrell, Anatol Bragin, Jerome Engel, Richard Staba, Michael Sperling

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations


Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy surgery planning offers the promise of improved seizure outcomes for patients with medically refractory epilepsy. This review discusses possible machine learning strategies that can be applied to HFO biomarkers to better identify epileptogenic regions. We discuss the role of HFO rate, and utilizing features such as explicit HFO properties (spectral content, duration, and power) and phase-amplitude coupling for distinguishing pathological HFO (pHFO) events from physiological HFO events. In addition, the review highlights the importance of neuroanatomical localization in machine learning strategies.

Original languageEnglish (US)
Pages (from-to)409-418
Number of pages10
JournalBiomarkers in Medicine
Issue number5
StatePublished - Apr 2019


  • HFO
  • artificial intelligence
  • epilepsy
  • epilepsy surgery
  • epileptiform spike
  • fast ripple
  • high-frequency oscillation
  • machine learning
  • phase-amplitude coupling
  • ripple
  • seizure
  • wavelet

ASJC Scopus subject areas

  • Drug Discovery
  • Clinical Biochemistry
  • Biochemistry, medical


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