Publications by authors named "S N Burnos"

High frequency oscillations (HFOs) are recognized as biomarkers for epileptogenic brain tissue. A remaining challenge for epilepsy surgery is the prospective classification of tissue sampled by individual electrode contacts. We analysed long-term invasive recordings of 20 consecutive patients who subsequently underwent epilepsy surgery.

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High frequency oscillations (HFOs, 80-500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80-250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort.

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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.

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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.

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Objective: The somatosensory evoked potential (SEP) elicited by median nerve stimulation consists of the N20 peak together with the concurrent high frequency oscillation (HFO, > 500 Hz). We describe the conditions for HFO detection in ECoG and scalp EEG in intraoperative recordings.

Methods: During neurosurgical interventions in six patients under propofol anesthesia, the SEP was recorded from subdural electrode strips (15 recordings) and from scalp electrodes (10/15 recordings).

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