We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centers. In this study, we combine retrospectively collected datasets from 9 independent epilepsy centers. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 minutes of NREM sleep. We automatically detect HFO in the ripple (80-250 Hz) and in the fast ripple (250-500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast-ripple and a ripple. We calculate the temporal consistency of each patient's HFO rates over several data epochs within and between nights. Patients with temporal consistency < 50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 y follow-up.~Applying a previously validated algorithm to a large cohort from several independent epilepsy centers may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.
In drug-resistant focal epilepsy, interictal high frequency oscillations (HFO) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom.~We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all datasets. The aim is to assess the reliability of the proposed detector and analysis approach.