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 endowed the SNN with a novel artifact rejection mechanism to suppress sharp transients and demonstrate its effectiveness on the ECoG dataset. The HFO rates (median 6.6 HFO/min in pre-resection recordings) detected by this SNN are comparable to those published in the dataset (Spearman’s rho = 0.81). The postsurgical seizure outcome was “predicted” with 100% (CI [63 100%]) accuracy for all 8 patients. These results provide a further step towards the construction of a real-time portable battery-operated HFO detection system that can be used during epilepsy surgery to guide the resection of the epileptogenic zone.
Highlights
Among patients with epilepsy, one-third have seizures that cannot be controlled by medication[1]
The value of high frequency oscillations (HFOs) for delineating the epileptogenic zone (EZ) must be confirmed in a large prospective clinical trial with a large numbers of patients recruited from multiple centers
Architecture of the core spiking neural network (SNN) augmented by the artifact rejection stage
Summary
One-third have seizures that cannot be controlled by medication[1]. Intraoperative electrocorticography (ECoG) can be performed during surgery to optimize the delineation of the epileptogenic zone (EZ) against healthy brain tissue by taking into account interictal spike patterns[3,4,5,6] This so called “tailoring” may guide surgical decisions, but the value of interictal spikes as an epilepsy biomarker in this context is under d ebate[7]. While there are many automated detection schemes that define HFOs prospectively, only few validated the detected HFOs against postsurgical seizure freedom[7,8,9,10,11,12,13] These detectors require further offline processing of the pre-recorded signal to apply an automatic or semiautomatic artifact rejection stage to eliminate events wrongly classified as HFO. The HFOs of the Spectrum detector have since been validated to predict seizure freedom in independently recorded d atasets[8,11]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
