Tommaso Fedele1, Maryse van 't Klooster2, Sergey Burnos3, Willemiek Zweiphenning2, Nicole van Klink2, Frans Leijten2, Maeike Zijlmans4, Johannes Sarnthein5. 1. Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland. Electronic address: tommaso.fedele@usz.ch. 2. Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands. 3. Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland; Institute of Neuroinformatics, ETH Zurich, Zurich, Switzerland. 4. Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands; SEIN-Stichting Epilepsie Instellingen Nederland, Heemstede, The Netherlands. 5. Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland.
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.
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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