Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-26T05:32:08.612Z Has data issue: false hasContentIssue false

Chapter 20 - Quantitative Intracranial EEG Signal Analysis in Insular Epilepsy

from Section 4 - Invasive Investigation of Insular Epilepsy

Published online by Cambridge University Press:  09 June 2022

By
Fabrice Bartolomei ,
Nicolas Roehri ,
Rinki Singh  and
Francesca Pizzo
Edited by
Dang Nguyen ,
Jean Isnard  and
Philippe Kahane
Dang Nguyen
Affiliation:
Université de Montréal
Jean Isnard
Affiliation:
Claude Bernard University Lyon
Philippe Kahane
Affiliation:
Grenoble-Alpes University Hospital
Get access

Summary

The accurate definition of the seizure onset zone (SOZ) is a central problem in drug-resistant epilepsies. Signal processing methods may be used to complement the interpretation of SEEG recordings by quantifying interictal events and SOZ. In this chapter, we will describe some of these methods and focus on insular epilepsies with some detailed case examples.

Keywords

insulo-opercular epilepsySEEGsignal analysishigh frequency oscillationsepileptogenicity indexepileptogenicity maps
Type
Chapter
Information
Insular Epilepsies , pp. 238 - 245
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Wendling, F, Benquet, P, Bartolomei, F. SEEG recordings: From signal processing to models of epileptogenic networks. In Latoo, S, Kahane, P, Luders, H (eds.), Invasive studies of the human epileptic brain, Oxford University Press: Oxford; 2019: 520–534.Google Scholar
Bartolomei, F, Nica, A, MP, Valenti-Hirsch, Adam, C, Denuelle, M. Interpretation of SEEG recordings. Neurophysiol Clin 2018;48(1):53–57.Google Scholar
Gotman, J. Automatic detection of seizures and spikes. J Clin Neurophysiol 1999;16:130–140.CrossRefGoogle ScholarPubMed
Senhadji, L, Wendling, F. Epileptic transient detection: Wavelets and time-frequency approaches. Neurophysiol Clin 2002;32:175–192.Google Scholar
Brown, MW, III, Porter, BE, Dlugos, DJ, et al. Comparison of novel computer detectors and human performance for spike detection in intracranial EEG. Clin Neurophysiol 2007;118:1744–1752.CrossRefGoogle ScholarPubMed
Jacobs, J, Zijlmans, M, Zelmann, R, et al. High-frequency electroencephalographic oscillations correlate with outcome of epilepsy surgery. Ann Neurol 2010;67:209–220.Google Scholar
Benar, CG, Chauviere, L, Bartolomei, F, et al. Pitfalls of high-pass filtering for detecting epileptic oscillations: a technical note on “false” ripples. Clin Neurophysiol 2010;121:301–310.Google Scholar
Staba, RJ, Wilson, CL, Bragin, A, et al. Quantitative analysis of high-frequency oscillations (80–500 Hz) recorded in human epileptic hippocampus and entorhinal cortex. J Neurophysiol 2002;88:1743–1752.Google Scholar
Gardner, AB, Worrell, GA, Marsh, E, et al. Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings. Clin Neurophysiol 2007;118:1134–1143.Google Scholar
Crepon, B, Navarro, V, Hasboun, D, et al. Mapping interictal oscillations greater than 200 Hz recorded with intracranial macroelectrodes in human epilepsy. Brain 2010;133:33–45.Google Scholar
Birot, G, Kachenoura, A, Albera, L, et al. Automatic detection of fast ripples. J Neurosci Methods 2013;213:236–249.Google Scholar
Roehri, N, Pizzo, F, Lagarde, S, et al. High-frequency oscillations are not better biomarkers of epileptogenic tissues than spikes. Ann Neurol 2018;83(1):84–97.Google Scholar
Roehri, N, Bartolomei, F. Are high-frequency oscillations better biomarkers of the epileptogenic zone than spikes? Curr Opin Neurol 2019;32:213–219.Google Scholar
Roehri, N, Pizzo, F, Lagarde, S, et al. High-frequency oscillations are not better biomarkers of epileptogenic tissues than spikes. Ann Neurol 2018;83:84–97.CrossRefGoogle ScholarPubMed
Perucca, P, Dubeau, F, Gotman, J. Intracranial electroencephalographic seizure-onset patterns: Effect of underlying pathology. Brain 2014;137:183–196.CrossRefGoogle ScholarPubMed
Lagarde, S, Buzori, S, Trebuchon, A, et al. The repertoire of seizure onset patterns in human focal epilepsies: Determinants and prognostic values. Epilepsia 2019;60:85–95.Google Scholar
Alarcon, G, Binnie, CD, Elwes, RD, et al. Power spectrum and intracranial EEG patterns at seizure onset in partial epilepsy. Electroencephalogr Clin Neurophysiol 1995;94:326–337.Google Scholar
Bartolomei, F, Chauvel, P, Wendling, F. Epileptogenicity of brain structures in human temporal lobe epilepsy: a quantified study from intracerebral EEG. Brain 2008;131:1818–1830.CrossRefGoogle ScholarPubMed
Andrzejak, RG, David, O, Gnatkovsky, V, et al. Localization of epileptogenic zone on pre-surgical intracranial EEG recordings: Toward a validation of quantitative signal analysis approaches. Brain Topogr 2015;28:832–837.CrossRefGoogle Scholar
Gnatkovsky, V, de Curtis, M, Pastori, C, et al. Biomarkers of epileptogenic zone defined by quantified stereo-EEG analysis. Epilepsia 2014;55:296–305.CrossRefGoogle ScholarPubMed
David, O, Blauwblomme, T, Job, AS, et al. Imaging the seizure onset zone with stereo-electroencephalography. Brain 2011;134:2898–2911.CrossRefGoogle ScholarPubMed
Grinenko, O, Li, J, Mosher, JC, Wang, IZ, Bulacio, JC, Gonzalez-Martinez, J, Chauvel PA. Fingerprint of the epileptogenic zone in human epilepsies. Brain 2018;141:117–131.CrossRefGoogle ScholarPubMed
Marchi, A, Bonini, F, Lagarde, S, et al. Occipital and occipital “plus” epilepsies: A study of involved epileptogenic networks through SEEG quantification. Epilepsy Behav 2016;62:104–114.Google Scholar
Medina Villalon, S, Paz, R, Roehri, N, et al. EpiTools, a software suite for presurgical brain mapping in epilepsy: Intracerebral EEG. J Neurosci Methods 2018;303:7–15.Google Scholar
Bartolomei, F, Cosandier-Rimele, D, McGonigal, A, et al. From mesial temporal lobe to temporoperi-Sylvian seizures: A quantified study of temporal lobe seizure networks. Epilepsia 2010;51:2147–2158.CrossRefGoogle ScholarPubMed
Bartolomei, F, Wendling, F, Bellanger, J, et al. Neural networks involved in temporal lobe seizures: A nonlinear regression analysis of SEEG signals interdependencies. Clin Neurophysiol 2001;112:1746–1760.CrossRefGoogle Scholar
Hagiwara, K, Jung, J, Bouet, R, et al. How can we explain the frontal presentation of insular lobe epilepsy? The impact of non-linear analysis of insular seizures. Clin Neurophysiol 2017;128:780–791.Google Scholar
Blauwblomme, T, David, O, Minotti, L, et al. Prognostic value of insular lobe involvement in temporal lobe epilepsy: a stereoelectroencephalographic study. Epilepsia 2013;54:1658–1667.Google Scholar
Colombet, B, Woodman, M, Badier, JM, et al. AnyWave: A cross-platform and modular software for visualizing and processing electrophysiological signals. J Neurosci Methods 2015;242:118–126.CrossRefGoogle ScholarPubMed
Roehri, N, Lina, JM, Mosher, JC, et al. Time-frequency strategies for increasing high-frequency oscillation detectability in intracerebral EEG. IEEE Trans Biomed Eng 2016;63:2595–2606.CrossRefGoogle ScholarPubMed
Roehri, N, Pizzo, F, Bartolomei, F, et al. What are the assets and weaknesses of HFO detectors? A benchmark framework based on realistic simulations. PLoS One 2017;12:e0174702.CrossRefGoogle ScholarPubMed
Kini, LG, Bernabei, JM, Mikhail, F, et al. Virtual resection predicts surgical outcome for drug-resistant epilepsy. Brain 2019 Dec;142(12):3892–3905.Google Scholar
Olmi, S, Petkoski, S, Guye, M, et al. Controlling seizure propagation in large-scale brain networks. PLoS Comput Biol 2019;15:e1006805.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×