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 Jr; Christos Papadelis; Lin Li

doi:10.1002/epi4.12647

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

Epilepsia Open. 2022 Dec;7(4):674-686. doi: 10.1002/epi4.12647. Epub 2022 Sep 14.

Authors

Yufeng Zhou¹, Jing You¹, Udaya Kumar², Shennan A Weiss^{3

4}, Anatol Bragin^{2

5}, Jerome Engel Jr^{2

5

6

7}, Christos Papadelis^{8

9

10}, Lin Li^{1

2}

Affiliations

¹ Department of Biomedical Engineering, University of North Texas, Texas, USA.
² Department of Neurology, University of California Los Angeles, Los Angeles, California, USA.
³ Departments of Neurology, Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, USA.
⁴ Department of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, New York, USA.
⁵ Brain Research Institute, University of California, Los Angeles, California, USA.
⁶ Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
⁷ Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, California, USA.
⁸ Jane and John Justin Neurosciences Center, Cook Children's Health Care System, Fort Worth, Texas, USA.
⁹ School of Medicine, Texas Christian University, Fort Worth, Texas, USA.
¹⁰ Department of Bioengineering, University of Texas at Arlington, Arlington, Texas, USA.

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 $0.95 \pm 0.03$ , with precision, recall, and F1 scores of $0.92 \pm 0.05$ , $0.99 \pm 0.01$ , and $0.94 \pm 0.03$ , respectively. For the HFO classification, our algorithm obtained an accuracy of $0.97 \pm 0.02$ . For the inter-rater reliability of algorithm evaluation, the agreement among four experts was $0.94 \pm 0.03$ for HFO detection and $0.85 \pm 0.04$ 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.

Keywords: complex wavelet; epilepsy; high-frequency oscillations; topographical analysis.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Algorithms
Animals
Electroencephalography / methods
Epilepsy* / diagnosis
Epilepsy, Post-Traumatic*
Reproducibility of Results

Abstract

Publication types

MeSH terms

Grants and funding