An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

Mohammadali Sharifshazileh, Karla Burelo, Johannes Sarnthein () and Giacomo Indiveri ()
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Mohammadali Sharifshazileh: Institute of Neuroinformatics, University of Zurich and ETH Zurich
Karla Burelo: Institute of Neuroinformatics, University of Zurich and ETH Zurich
Johannes Sarnthein: University Hospital Zurich, University of Zurich
Giacomo Indiveri: Institute of Neuroinformatics, University of Zurich and ETH Zurich

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: Abstract The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.

Date: 2021
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DOI: 10.1038/s41467-021-23342-2

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