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An approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections
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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9712470/ https://www.ncbi.nlm.nih.gov/pubmed/36053171 http://dx.doi.org/10.1002/epi4.12647 |
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author | Zhou, Yufeng You, Jing Kumar, Udaya Weiss, Shennan A Bragin, Anatol Engel, Jerome Papadelis, Christos Li, Lin |
author_facet | Zhou, Yufeng You, Jing Kumar, Udaya Weiss, Shennan A Bragin, Anatol Engel, Jerome Papadelis, Christos Li, Lin |
author_sort | Zhou, Yufeng |
collection | PubMed |
description | 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: see text] , with precision, recall, and F1 scores of [Formula: see text] , [Formula: see text] , and [Formula: see text] , respectively. For the HFO classification, our algorithm obtained an accuracy of [Formula: see text]. For the inter‐rater reliability of algorithm evaluation, the agreement among four experts was [Formula: see text] for HFO detection and [Formula: see text] 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. |
format | Online Article Text |
id | pubmed-9712470 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97124702022-12-02 An approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections Zhou, Yufeng You, Jing Kumar, Udaya Weiss, Shennan A Bragin, Anatol Engel, Jerome Papadelis, Christos Li, Lin Epilepsia Open Original Articles 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: see text] , with precision, recall, and F1 scores of [Formula: see text] , [Formula: see text] , and [Formula: see text] , respectively. For the HFO classification, our algorithm obtained an accuracy of [Formula: see text]. For the inter‐rater reliability of algorithm evaluation, the agreement among four experts was [Formula: see text] for HFO detection and [Formula: see text] 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. John Wiley and Sons Inc. 2022-09-14 /pmc/articles/PMC9712470/ /pubmed/36053171 http://dx.doi.org/10.1002/epi4.12647 Text en © 2022 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles Zhou, Yufeng You, Jing Kumar, Udaya Weiss, Shennan A Bragin, Anatol Engel, Jerome Papadelis, Christos Li, Lin An approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections |
title | An approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections |
title_full | An approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections |
title_fullStr | An approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections |
title_full_unstemmed | An approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections |
title_short | An approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections |
title_sort | approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9712470/ https://www.ncbi.nlm.nih.gov/pubmed/36053171 http://dx.doi.org/10.1002/epi4.12647 |
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