A spiking neural network (SNN) for detecting high frequency oscillations (HFOs) in the intraoperative ECoG.
Karla Burelo,Mohammadali Sharifshazileh,Niklaus Krayenbühl,Georgia Ramantani,Georgia Ramantani,Giacomo Indiveri,Giacomo Indiveri,Johannes Sarnthein +7 more
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In this paper, a spiking neural network (SNN) was used to detect high frequency oscillations (HFOs) generated by epileptogenic tissue in intra-operative electrocorticography (ECoG) recordings.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.read more
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2022 roadmap on neuromorphic computing and engineering
TL;DR: In this article , the authors present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of the neuromorphic computing community.
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An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG.
TL;DR: In this article, the authors presented a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing intracranial EEG (iEEG) from epilepsy patients for the detection of high frequency oscillations (HFO), which are a biomarker for epileptogenic brain tissue.
Journal ArticleDOI
An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG
TL;DR: A neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network architecture on the same die for recording, processing, and detecting High Frequency Oscillations (HFO), which are biomarkers for the epileptogenic zone is presented.
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A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG
TL;DR: In this paper , a custom spike neural network (SNN) was designed to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band.
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