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An approach for reliably identifying high-frequency oscillations and reducing false-positive detections

  • Yufeng Zhou
  • , Jing You
  • , Udaya Kumar
  • , Shennan A. Weiss
  • , Anatol Bragin
  • , Jerome Engel
  • , Christos Papadelis
  • , Lin Li
  • University of North Texas
  • University of California at Los Angeles
  • Cook Children's Health Care System
  • Texas Christian University
  • University of Texas at Arlington

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Objective: Aiming to improve the feasibility and reliability of using high-frequency oscillations (HFOs) for translational studies of epilepsy, we present a pipeline with features specifically designed to reject false positives for HFOs to improve the automatic HFO detector. Methods: We presented an integrated, multi-layered procedure capable of automatically rejecting HFOs from a variety of common false positives, such as motion, background signals, and sharp transients. This method utilizes a time-frequency contour approach that embeds three different layers including peak constraints, power thresholds, and morphological identification to discard false positives. Four experts were involved in rating detected HFO events that were randomly selected from different posttraumatic epilepsy (PTE) animals for a comprehensive evaluation. Results: The algorithm was run on 768-h recordings of intracranial electrodes in 48 PTE animals. A total of 453 917 HFOs were identified by initial HFO detection, of which 450 917 were implemented for HFO refinement and 203 531 events were retained. Random sampling was used to evaluate the performance of the detector. The HFO detection yielded an overall accuracy of (Formula presented.), with precision, recall, and F1 scores of (Formula presented.), (Formula presented.), and (Formula presented.), respectively. For the HFO classification, our algorithm obtained an accuracy of (Formula presented.). For the inter-rater reliability of algorithm evaluation, the agreement among four experts was (Formula presented.) for HFO detection and (Formula presented.) for HFO classification. Significance: Our approach shows that a segregated pipeline design with a focus on false-positive rejection can improve the detection efficiency and provide reliable results. This pipeline does not require customization and uses fixed parameters, making it highly feasible and translatable for basic and clinical applications of epilepsy.

Original languageEnglish
Pages (from-to)674-686
Number of pages13
JournalEpilepsia Open
Volume7
Issue number4
DOIs
StatePublished - Dec 2022

Keywords

  • complex wavelet
  • epilepsy
  • high-frequency oscillations
  • topographical analysis

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