title: Universal automated high frequency oscillation detector for real-time, long term EEG.
abstract: OBJECTIVE
  Interictal high frequency oscillations (HFOs) in intracranial EEG are a potential biomarker of epilepsy, but current automated HFO detectors require human review to remove artifacts. Our objective is to automatically redact false HFO detections, facilitating clinical use of interictal HFOs.
  
  
  METHODS
  Intracranial EEG data from 23 patients were processed with automated detectors of HFOs and artifacts. HFOs not concurrent with artifacts were labeled quality HFOs (qHFOs). Methods were validated by human review on a subset of 2000 events. The correlation of qHFO rates with the seizure onset zone (SOZ) was assessed via (1) a retrospective asymmetry measure and (2) a novel quasi-prospective algorithm to identify SOZ.
  
  
  RESULTS
  Human review estimated that less than 12% of qHFOs are artifacts, whereas 78.5% of redacted HFOs are artifacts. The qHFO rate was more correlated with SOZ (p=0.020, Wilcoxon signed rank test) and resected volume (p=0.0037) than baseline detections. Using qHFOs, our algorithm was able to determine SOZ in 60% of the ILAE Class I patients, with all algorithmically-determined SOZs fully within the resected volumes.
  
  
  CONCLUSIONS
  The algorithm reduced false-positive HFO detections, improving the precision of the HFO-biomarker.
  
  
  CONCLUSIONS
  These methods provide a feasible strategy for HFO detection in real-time, continuous EEG with minimal human monitoring of data quality.
authors.affiliation: Department of Neurology, University of Michigan, USA. Electronic address: sgliske@umich.edu.
authors.forename: Stephen V
authors.initials: SV
authors.lastname: Gliske
authors.affiliation: Department of Biomedical Engineering, University of Michigan, USA. Electronic address: irwinz@umich.edu.
authors.forename: Zachary T
authors.initials: ZT
authors.lastname: Irwin
authors.affiliation: Department of Neurology, Hospital of the University of Pennsylvania, USA. Electronic address: Kathryn.davis@uphs.upenn.edu.
authors.forename: Kathryn A
authors.initials: KA
authors.lastname: Davis
authors.affiliation: Department of Neurology, University of Arkansas for Medical Sciences, USA. Electronic address: ksahaya@uams.edu.
authors.forename: Kinshuk
authors.initials: K
authors.lastname: Sahaya
authors.affiliation: Department of Biomedical Engineering, University of Michigan, USA. Electronic address: cchestek@umich.edu.
authors.forename: Cynthia
authors.initials: C
authors.lastname: Chestek
authors.affiliation: Department of Neurology, University of Michigan, USA; Department of Biomedical Engineering, University of Michigan, USA. Electronic address: william.stacey@umich.edu.
authors.forename: William C
authors.initials: WC
authors.lastname: Stacey
mesh_terms: D000328:Adult; D016477:Artifacts; D058256:Brain Waves / Q000502:physiology; D003199:Computer Systems / Q000639:trends*; D004569:Electroencephalography / Q000639:trends*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D013997:Time Factors; D055815:Young Adult
keywords: Artifact detector; Automated detector; High frequency oscillation; Ripple; Seizure onset zone