EPINETLAB: a software for seizure-onset zone identification from intracranial EEG signal in epilepsy

Lucia Rita Quitadamo, Elaine Foley, Roberto Mai, Luca De Palma, Nicola Specchio, Stefano Seri

Research output: Contribution to journalArticle

Abstract

The pre-operative workup of patients with drug-resistant epilepsy requires in some candidates the identification from intracranial EEG (iEEG) of the seizure-onset zone (SOZ), defined as the area responsible of the generation of the seizure and therefore candidate for resection. High-frequency oscillations (HFOs) contained in the iEEG signal have been proposed as biomarker of the SOZ. Their visual identification is a very onerous process and an automated detection tool could be an extremely valuable aid for clinicians, reducing operator-dependent bias and computational time.

In this manuscript we present the EPINETLAB software, developed as a collection of routines integrated in the EEGLAB framework that aim to provide clinicians with a structured analysis pipeline for HFOs detection and SOZ identification. The tool implements an analysis strategy developed by our group and underwent a preliminary clinical validation that identifies the HFOs area by extracting the statistical properties of HFOs signal and that provides useful information for a topographic characterization of the relationship between clinically defined SOZ and HFO area. Additional functionalities such as inspection of spectral properties of ictal iEEG data and import and analysis of source-space MEG data were also included.

EPINETLAB was developed with user-friendliness in mind to support clinicians in the identification and quantitative assessment of HFOs in iEEG and source space MEG data and aid the evaluation of the SOZ for pre-surgical assessment.
LanguageEnglish
Article number45
JournalFrontiers in Neuroinformatics
Volume12
DOIs
Publication statusPublished - 11 Jul 2018

Fingerprint

Electroencephalography
Epilepsy
Seizures
Software
Biomarkers
Pipelines
Inspection
Electrocorticography
Stroke

Bibliographical note

© 2018 Quitadamo, Foley, Mai, de Palma, Specchio and Seri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Funding: This study has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant agreement No. 655016.

Keywords

  • EEGLAB
  • Epilepsy
  • high-frequency oscillations
  • Seizure-onset zone
  • iEEG
  • stereo-EEG

Cite this

@article{b664ea31dc80428091198bcfca4b4fda,
title = "EPINETLAB: a software for seizure-onset zone identification from intracranial EEG signal in epilepsy",
abstract = "The pre-operative workup of patients with drug-resistant epilepsy requires in some candidates the identification from intracranial EEG (iEEG) of the seizure-onset zone (SOZ), defined as the area responsible of the generation of the seizure and therefore candidate for resection. High-frequency oscillations (HFOs) contained in the iEEG signal have been proposed as biomarker of the SOZ. Their visual identification is a very onerous process and an automated detection tool could be an extremely valuable aid for clinicians, reducing operator-dependent bias and computational time. In this manuscript we present the EPINETLAB software, developed as a collection of routines integrated in the EEGLAB framework that aim to provide clinicians with a structured analysis pipeline for HFOs detection and SOZ identification. The tool implements an analysis strategy developed by our group and underwent a preliminary clinical validation that identifies the HFOs area by extracting the statistical properties of HFOs signal and that provides useful information for a topographic characterization of the relationship between clinically defined SOZ and HFO area. Additional functionalities such as inspection of spectral properties of ictal iEEG data and import and analysis of source-space MEG data were also included. EPINETLAB was developed with user-friendliness in mind to support clinicians in the identification and quantitative assessment of HFOs in iEEG and source space MEG data and aid the evaluation of the SOZ for pre-surgical assessment.",
keywords = "EEGLAB, Epilepsy, high-frequency oscillations, Seizure-onset zone, iEEG, stereo-EEG",
author = "Quitadamo, {Lucia Rita} and Elaine Foley and Roberto Mai and {De Palma}, Luca and Nicola Specchio and Stefano Seri",
note = "{\circledC} 2018 Quitadamo, Foley, Mai, de Palma, Specchio and Seri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Funding: This study has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant agreement No. 655016.",
year = "2018",
month = "7",
day = "11",
doi = "10.3389/fninf.2018.00045",
language = "English",
volume = "12",

}

EPINETLAB : a software for seizure-onset zone identification from intracranial EEG signal in epilepsy. / Quitadamo, Lucia Rita; Foley, Elaine; Mai, Roberto ; De Palma, Luca; Specchio, Nicola; Seri, Stefano.

Vol. 12, 45, 11.07.2018.

Research output: Contribution to journalArticle

TY - JOUR

T1 - EPINETLAB

T2 - a software for seizure-onset zone identification from intracranial EEG signal in epilepsy

AU - Quitadamo, Lucia Rita

AU - Foley, Elaine

AU - Mai, Roberto

AU - De Palma, Luca

AU - Specchio, Nicola

AU - Seri, Stefano

N1 - © 2018 Quitadamo, Foley, Mai, de Palma, Specchio and Seri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Funding: This study has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant agreement No. 655016.

PY - 2018/7/11

Y1 - 2018/7/11

N2 - The pre-operative workup of patients with drug-resistant epilepsy requires in some candidates the identification from intracranial EEG (iEEG) of the seizure-onset zone (SOZ), defined as the area responsible of the generation of the seizure and therefore candidate for resection. High-frequency oscillations (HFOs) contained in the iEEG signal have been proposed as biomarker of the SOZ. Their visual identification is a very onerous process and an automated detection tool could be an extremely valuable aid for clinicians, reducing operator-dependent bias and computational time. In this manuscript we present the EPINETLAB software, developed as a collection of routines integrated in the EEGLAB framework that aim to provide clinicians with a structured analysis pipeline for HFOs detection and SOZ identification. The tool implements an analysis strategy developed by our group and underwent a preliminary clinical validation that identifies the HFOs area by extracting the statistical properties of HFOs signal and that provides useful information for a topographic characterization of the relationship between clinically defined SOZ and HFO area. Additional functionalities such as inspection of spectral properties of ictal iEEG data and import and analysis of source-space MEG data were also included. EPINETLAB was developed with user-friendliness in mind to support clinicians in the identification and quantitative assessment of HFOs in iEEG and source space MEG data and aid the evaluation of the SOZ for pre-surgical assessment.

AB - The pre-operative workup of patients with drug-resistant epilepsy requires in some candidates the identification from intracranial EEG (iEEG) of the seizure-onset zone (SOZ), defined as the area responsible of the generation of the seizure and therefore candidate for resection. High-frequency oscillations (HFOs) contained in the iEEG signal have been proposed as biomarker of the SOZ. Their visual identification is a very onerous process and an automated detection tool could be an extremely valuable aid for clinicians, reducing operator-dependent bias and computational time. In this manuscript we present the EPINETLAB software, developed as a collection of routines integrated in the EEGLAB framework that aim to provide clinicians with a structured analysis pipeline for HFOs detection and SOZ identification. The tool implements an analysis strategy developed by our group and underwent a preliminary clinical validation that identifies the HFOs area by extracting the statistical properties of HFOs signal and that provides useful information for a topographic characterization of the relationship between clinically defined SOZ and HFO area. Additional functionalities such as inspection of spectral properties of ictal iEEG data and import and analysis of source-space MEG data were also included. EPINETLAB was developed with user-friendliness in mind to support clinicians in the identification and quantitative assessment of HFOs in iEEG and source space MEG data and aid the evaluation of the SOZ for pre-surgical assessment.

KW - EEGLAB

KW - Epilepsy

KW - high-frequency oscillations

KW - Seizure-onset zone

KW - iEEG

KW - stereo-EEG

UR - https://www.frontiersin.org/articles/10.3389/fninf.2018.00045/abstract

U2 - 10.3389/fninf.2018.00045

DO - 10.3389/fninf.2018.00045

M3 - Article

VL - 12

M1 - 45

ER -