High-frequency oscillations detected in epileptic networks using swarmed neural-network features

Localizing epileptic networks is a central challenge in guiding epilepsy surgery, deploying antiepileptic devices, and elucidating mechanisms underlying seizure generation. Recent work from our group and others suggests that high-frequency epileptic oscillations (HFEOs) arise from brain regions constituting epileptic networks, and may be important to seizure generation. HFEOs are brief 50-500 Hz pathologic events measured in intracranial field and unit recordings in patients with refractory epilepsy. They are challenging to detect due to low signal to noise ratio, and because they occur in multiple channels with great frequency. Their morphology is also variable and changes with distance from intracranial electrode contacts, which are sparsely placed for patient safety. Thus reliable, automated methods to detect HFEOs are required to localize and track seizure generation in epileptic networks. We present a novel method for mapping the temporal evolution of these oscillations in human epileptic networks. The technique combines a particle swarm optimization algorithm with a neural network to create features that robustly detect and track HFEOs in human intracranial EEG (IEEG) recordings. We demonstrate the algorithm's performance on IEEG data from six patients, one pediatric and five adult, and compare it to an existing method for detecting high-frequency oscillations.