Tommaso Fedele1, Georgia Ramantani2, Sergey Burnos3, Peter Hilfiker2, Gabriel Curio4, Thomas Grunwald2, Niklaus Krayenbühl3, Johannes Sarnthein5. 1. University Hospital Zurich, Neurosurgery Department, Zurich, Switzerland. Electronic address: tommaso.fedele@usz.ch. 2. Swiss Epilepsy Centre Department, Zurich, Switzerland. 3. University Hospital Zurich, Neurosurgery Department, Zurich, Switzerland. 4. Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité, Berlin, Germany. 5. University Hospital Zurich, Neurosurgery Department, Zurich, Switzerland; Neuroscience Center Zurich, ETH Zurich, Zurich, Switzerland.
Abstract
OBJECTIVE: Fast ripples (FR, 250-500Hz) in the intraoperative corticogram have recently been proposed as specific predictors of surgical outcome in epilepsy patients. However, online FR detection is restricted by their low signal-to-noise ratio. Here we propose the integration of low-noise EEG with unsupervised FR detection. METHODS: Pre- and post-resection ECoG (N=9 patients) was simultaneously recorded by a commercial device (CD) and by a custom-made low-noise amplifier (LNA). FR were analyzed by an automated detector previously validated on visual markings in a different dataset. RESULTS: Across all recordings, in the FR band the background noise was lower in LNA than in CD (p<0.001). FR rates were higher in LNA than CD recordings (0.9±1.4 vs 0.4±0.9, p<0.001). Comparison between FR rates in post-resection ECoG and surgery outcome resulted in positive predictive value PPV=100% in CD and LNA, and negative predictive value NPV=38% in CD and NPV=50% for LNA. Prediction accuracy was 44% for CD and 67% for LNA. CONCLUSIONS: Prediction of seizure outcome was improved by the optimal integration of low-noise EEG and unsupervised FR detection. SIGNIFICANCE: Accurate, automated and fast FR rating is essential for consideration of FR in the intraoperative setting.
OBJECTIVE: Fast ripples (FR, 250-500Hz) in the intraoperative corticogram have recently been proposed as specific predictors of surgical outcome in epilepsypatients. However, online FR detection is restricted by their low signal-to-noise ratio. Here we propose the integration of low-noise EEG with unsupervised FR detection. METHODS: Pre- and post-resection ECoG (N=9 patients) was simultaneously recorded by a commercial device (CD) and by a custom-made low-noise amplifier (LNA). FR were analyzed by an automated detector previously validated on visual markings in a different dataset. RESULTS: Across all recordings, in the FR band the background noise was lower in LNA than in CD (p<0.001). FR rates were higher in LNA than CD recordings (0.9±1.4 vs 0.4±0.9, p<0.001). Comparison between FR rates in post-resection ECoG and surgery outcome resulted in positive predictive value PPV=100% in CD and LNA, and negative predictive value NPV=38% in CD and NPV=50% for LNA. Prediction accuracy was 44% for CD and 67% for LNA. CONCLUSIONS: Prediction of seizure outcome was improved by the optimal integration of low-noise EEG and unsupervised FR detection. SIGNIFICANCE: Accurate, automated and fast FR rating is essential for consideration of FR in the intraoperative setting.
Authors: Shaun A Hussain; Gary W Mathern; Phoebe Hung; Julius Weng; Raman Sankar; Joyce Y Wu Journal: Epilepsy Res Date: 2017-06-16 Impact factor: 3.045
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Authors: Willemiek J E M Zweiphenning; Nicolás von Ellenrieder; François Dubeau; Laurence Martineau; Lorella Minotti; Jeffery A Hall; Stephan Chabardes; Roy Dudley; Philippe Kahane; Jean Gotman; Birgit Frauscher Journal: Epilepsia Date: 2021-12-16 Impact factor: 6.740