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

Mohammadali Sharifshazileh,Johannes Sarnthein,Giacomo Indiveri,Karla Burelo

doi:10.1038/s41467-021-23342-2

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.

Highlights

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
Neural recording headstages for experimental neuroscience target highdensity recordings[20,21,22,23] and minimize the circuit area requirements, while devices used for clinical studies and therapeutic applications require a small number of recording channels and the highest possible signal-to-noise ratio (SNR)[24,25,26,27]
We show how the neuromorphic system performs in High Frequency Oscillations (HFO) detection compared to the Morphology Detector[31] on intracranial EEG (iEEG) recorded from the medial temporal lobe[43]

Summary

Introduction

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. 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. Several innovative neuromorphic computing devices have been developed to carry out computation “at the edge”[3,4,5,6,7,8] These are general purpose brain-inspired architectures that support the implementation of spiking and rate-based neural networks for solving a wide range of spatio-temporal pattern recognition problems[9,10].

Methods

Results

Conclusion