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

Publication date: Available online 18 June 2016
Source:Clinical Neurophysiology
Author(s): Tommaso Fedele, Maryse van't Klooster, Sergey Burnos, Willemiek Zweiphenning, Nicole van Klink, Frans Leijten, Maeike Zijlmans, Johannes Sarnthein
ObjectiveHigh 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 is prone to observer bias. We validate here a fully automatic HFO detector against seizure outcome.MethodsPre-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 (80-250 Hz) and FRs (250-500 Hz).ResultsChannel-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%.ConclusionsOur automatic and fully unsupervised detection of HFO events matched the expert observer's performance in both event selection and outcome prediction.SignificanceThe detector provides a standardized definition of clinically relevant HFOs, which may spread its use in clinical application.



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