Evidence map›Paper›PMID 33762590›Full record

ArticleScientific reports2021

A spiking neural network (SNN) for detecting high frequency oscillations (HFOs) in the intraoperative ECoG.

Karla Burelo, Mohammadali Sharifshazileh, Niklaus Krayenbühl, Georgia Ramantani, Giacomo Indiveri, Johannes Sarnthein

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In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Association between Removal of High-Frequency Oscillations and the Effect of Epilepsy Surgery: A Meta-Analysis.Journal of neurological surgery. Part A, Central European neurosurgery · 2024
    Pooled it
  2. Article
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  8. A real-time HFO detection framework for localization of SOZ in iEEG recording of patients with epilepsy.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024
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  10. Networks through the lens of high-frequency oscillations.Frontiers in network physiology · 2024
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Karla BureloInstitute of Neuroinformatics, University of Zurich and ETH Zurich, 8057, Zurich, Switzerland.
Mohammadali SharifshazilehInstitute of Neuroinformatics, University of Zurich and ETH Zurich, 8057, Zurich, Switzerland.
Niklaus KrayenbühlUniversity Children's Hospital, University of Zurich, 8032, Zurich, Switzerland.
Georgia RamantaniUniversity Children's Hospital, University of Zurich, 8032, Zurich, Switzerland.
Giacomo IndiveriInstitute of Neuroinformatics, University of Zurich and ETH Zurich, 8057, Zurich, Switzerland.
Johannes SarntheinDepartment of Neurosurgery, University Hospital Zurich, University of Zurich, 8091, Zurich, Switzerland. johannes.sarnthein@usz.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 [Formula: see text] = 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.

Identifiers

PMID33762590
PMCPMC7990937

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.