Quantification of spontaneous and evoked HFO's in SEEG recording and prospective for pre-surgical diagnostics. Case study.

High frequency oscillations (HFO) in stereo electroencephalographic (SEEG) signals have been recently the focus of attention as biomarkers that can have potential predictive power for the spatial location and possibly the timing of the onset of epileptic seizures. In this work we present a case study where we compare two quantitative paradigms for automated detection of biomarkers, one based on spontaneous SEEG recordings of HFOs and the other using activity induced by direct electrical stimulation (relative Phase Clustering Index algorithm). We compare the performance of these automated methods with manually detected HFO ripples by a trained EEG analyst and explore their potential diagnostic relevance.