Using Automated Detection and Classification of Interictal HFOs to Improve the Identification of Epileptogenic Zones in Preparation for Epilepsy Surgery

ObjectiveFor more than 25 million drug-resistant epilepsy patients, surgical intervention aiming at resecting brain regions where seizures arise is often the only alternative therapy. However, the identification of this epileptogenic zone (EZ) is often imprecise which may affect post-surgical outcomes (PSOs). Interictal high-frequency oscillations (HFOs) have been revealed to be reliable biomarkers in delineating EZ. In this paper, an analytical methodology aiming at automated detection and classification of interictal HFOs is proposed to improve the identification of EZ. Furthermore, the detected high-rate HFO areas were compared with the seizure onset zones (SOZs) and resected areas to investigate their clinical relevance in predicting PSOs.\n\nMethodsFIR band-pass filtering as well as a combination of time-series local energy, peak, and duration analysis were utilized to identify high-rate HFO areas in interictal, multi-channel intracranial electroencephalographic (iEEG) recordings. The detected HFOs were then classified into fast-ripple (FR), ripple (R), and fast-ripple concurrent with ripple (FRandR) events.\n\nResultsThe proposed method resulted in sensitivity of 91.08% and false discovery rate of 7.32%. Moreover, it was found that the detected HFO-FRandR areas in concordance with the SOZs would have better delineated the EZ for each patient, while limiting the area of the brain required to be resected.\n\nConclusionTesting on a dataset of 20 patients has supported the feasibility of using this method to provide an automated algorithm to better delineate the EZ.\n\nSignificanceThe proposed methodology may significantly improve the precision by which pathological brain tissue can be identified.