A spiking neural network (SNN) for detecting high frequency oscillations (HFOs) in the intraoperative ECoG.

ABSTRACT: To achieve seizure freedom, epilepsy surgery requires the complete resection of the epileptogenic brain tissue. In intraoperative electrocorticography (ECoG) recordings, high frequency oscillations (HFOs) generated by epileptogenic tissue can be used to tailor the resection margin. However, automatic detection of HFOs in real-time remains an open challenge. Here we present a spiking neural network (SNN) for automatic HFO detection that is optimally suited for neuromorphic hardware implementation. We trained the SNN to detect HFO signals measured from intraoperative ECoG on-line, using an independently labeled dataset (58 min, 16 recordings). We targeted the detection of HFOs in the fast ripple frequency range (250-500 Hz) and compared the network results with the labeled HFO data. We endow

SUBMITTER: Burelo K 

PROVIDER: S-EPMC7990937 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature