A Spiking Neural Network (SNN) for detecting High Frequency Oscillations (HFOs) in the intraoperative ECoG

TL;DR

This paper introduces a spiking neural network designed for real-time detection of high frequency oscillations in intraoperative ECoG, aiding epilepsy surgery by accurately identifying epileptogenic tissue.

Contribution

The study presents a novel SNN with artifact rejection for real-time HFO detection on neuromorphic hardware, demonstrating high accuracy in predicting surgical outcomes.

Findings

HFO detection rates comparable to existing datasets

100% accuracy in predicting post-surgical seizure outcomes

Effective artifact rejection mechanism implemented

Abstract

To achieve seizure freedom, epilepsy surgery requires the complete resection of the epileptogenic brain tissue. In intraoperative 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. 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 (58 min, 16 recordings). The postsurgical seizure outcome was "predicted" with 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.