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  • Title: A novel HFO-based method for unsupervised localization of the seizure onset zone in drug-resistant epilepsy.
    Author: Murphy PM, von Paternos AJ, Santaniello S.
    Journal: Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():1054-1057. PubMed ID: 29060055.
    Abstract:
    High frequency oscillations (HFOs) are potential biomarkers of epileptic areas. In patients with drug-resistant epilepsy, HFO rates tend to be higher in the seizure onset zone (SOZ) than in other brain regions and the resection of HFO-generating areas positively correlates with seizure-free surgery outcome. Nonetheless, the development of robust unsupervised HFO-based tools for SOZ localization remains challenging. Current approaches predict the SOZ by processing small samples of intracranial EEG (iEEG) data and applying patient-specific thresholds on the HFO rate. The HFO rate, though, varies largely over time with the patient's conditions (e.g., sleep versus wakefulness) and across patients. We propose a novel localization method for SOZ that uses a time-varying, HFO-based index to estimate the epileptic susceptibility of the iEEG channels. The method is insensitive to the average HFO rate across channels (which is both patient- and condition-specific), tracks the channel susceptibility over time, and predicts the SOZ based on the temporal evolution of the HFO rate. Tested on a preliminary dataset of continuous multi-day multichannel interictal iEEG recordings from two epileptic patients (117±97.6 h/per patient, mean ± S.D.), the reported SOZ prediction had an average 0.70±0.18 accuracy and 0.67±0.07 area under the ROC curve (mean ± S.D.) across patients.
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