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Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome


Fedele, Tommaso; van ’t Klooster, Maryse; Burnos, Sergey; Zweiphenning, Willemiek; van Klink, Nicole; Leijten, Frans; Zijlmans, Maeike; Sarnthein, Johannes (2016). Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome. Clinical Neurophysiology, 127(9):3066-3074.

Abstract

OBJECTIVE: High frequency oscillations (HFOs) and in particular fast ripples (FRs) in the post-resection electrocorticogram (ECoG) have recently been shown to be highly specific predictors of outcome of epilepsy surgery. FR visual marking is time consuming and prone to observer bias. We validate here a fully automatic HFO detector against seizure outcome. METHODS: Pre-resection ECoG dataset (N=14 patients) with visually marked HFOs were used to optimize the detector's parameters in the time-frequency domain. The optimized detector was then applied on a larger post-resection ECoG dataset (N=54) and the output was compared with visual markings and seizure outcome. The analysis was conducted separately for ripples (80-250Hz) and FRs (250-500Hz). RESULTS: Channel-wise comparison showed a high association between automatic detection and visual marking (p<0.001 for both FRs and ripples). Automatically detected FRs were predictive of clinical outcome with positive predictive value PPV=100% and negative predictive value NPV=62%, while for ripples PPV=43% and NPV=100%. CONCLUSIONS: Our automatic and fully unsupervised detection of HFO events matched the expert observer's performance in both event selection and outcome prediction. SIGNIFICANCE: The detector provides a standardized definition of clinically relevant HFOs, which may spread its use in clinical application.

Abstract

OBJECTIVE: High frequency oscillations (HFOs) and in particular fast ripples (FRs) in the post-resection electrocorticogram (ECoG) have recently been shown to be highly specific predictors of outcome of epilepsy surgery. FR visual marking is time consuming and prone to observer bias. We validate here a fully automatic HFO detector against seizure outcome. METHODS: Pre-resection ECoG dataset (N=14 patients) with visually marked HFOs were used to optimize the detector's parameters in the time-frequency domain. The optimized detector was then applied on a larger post-resection ECoG dataset (N=54) and the output was compared with visual markings and seizure outcome. The analysis was conducted separately for ripples (80-250Hz) and FRs (250-500Hz). RESULTS: Channel-wise comparison showed a high association between automatic detection and visual marking (p<0.001 for both FRs and ripples). Automatically detected FRs were predictive of clinical outcome with positive predictive value PPV=100% and negative predictive value NPV=62%, while for ripples PPV=43% and NPV=100%. CONCLUSIONS: Our automatic and fully unsupervised detection of HFO events matched the expert observer's performance in both event selection and outcome prediction. SIGNIFICANCE: The detector provides a standardized definition of clinically relevant HFOs, which may spread its use in clinical application.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Neurosurgery
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Epilepsy surgery, Intraoperative ECoG, High frequency oscillations, Fast ripples, Automatic detection, Seizure outcome
Language:English
Date:September 2016
Deposited On:23 Nov 2016 15:47
Last Modified:19 Feb 2018 21:42
Publisher:Elsevier
ISSN:1388-2457
OA Status:Green
Publisher DOI:https://doi.org/10.1016/j.clinph.2016.06.009
PubMed ID:27472542

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