Detecting high‐frequency oscillations in real time during epilepsy surgery with neuromorphic hardware validated to predict postoperative seizure outcome

OBJECTIVE: High-frequency oscillations (HFOs; 250-500 Hz) in electrocorticography (ECoG) are promising biomarkers for delineating the epileptogenic zone during epilepsy surgery. Accurate, intraoperative real-time detection of HFOs may guide surgical resection to improve seizure outcome. METHODS: We used intraoperative ECoG from 22 patients from Zurich (Nicolet recording device, recording under Sevoflurane) with annotated HFOs to train an innovative automated HFO detector implemented in a spiking neural network (SNN) on neuromorphic hardware. We validated whether the detector could identify clinically relevant HFOs in an independent cohort of 54 patients from Utrecht (Micromed recording device, recording during a pause in propofol administration) by examining whether HFOs remained after resection and whether this predicted seizure outcome. We then tested the detector in real time during surgery, giving direct feedback to the neurosurgeon. RESULTS: The optimal detection parameters yielded Spearman R = .71 between channelwise HFO rates detected by the proposed SNN detection and those previously detected by the Spectrum Detector in the training dataset. We found postresection HFOs in nine patients who had poor surgery outcome and in one patient with good surgery outcome in the validation data, resulting in a diagnostic odds ratio = 17 (95% confidence interval = 2.0-150). During a live surgery, the detector analyzed incoming intraoperative ECoG during acquisition, HFOs were successfully captured, and HFO feedback was available within 1 min after completion of the recording period. SIGNIFICANCE: Our automatic detector was easily transferred between centers and anesthesia protocols. We showed the direct use of intraoperative HFO detection as possible real-time feedback to the surgeon to tailor resection, yielding high predictive power of seizure outcome.