Nature Communications (Apr 2024)

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework

  • Filippo Costa,
  • Eline V. Schaft,
  • Geertjan Huiskamp,
  • Erik J. Aarnoutse,
  • Maryse A. van’t Klooster,
  • Niklaus Krayenbühl,
  • Georgia Ramantani,
  • Maeike Zijlmans,
  • Giacomo Indiveri,
  • Johannes Sarnthein

DOI
https://doi.org/10.1038/s41467-024-47495-y
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 12

Abstract

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Abstract Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman’s ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.