Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis.

<h4>Objectives</h4>High frequency oscillations (HFOs) have been proposed as a new biomarker for epileptogenic tissue. The exact characteristics of clinically relevant HFOs and their detection are still to be defined.<h4>Methods</h4>We propose a new method for HFO detection, w...

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Autores principales:	Sergey Burnos, Peter Hilfiker, Oguzkan Sürücü, Felix Scholkmann, Niklaus Krayenbühl, Thomas Grunwald, Johannes Sarnthein
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/960d25274e174fa39db2a0dfa1218a65
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spelling	oai:doaj.org-article:960d25274e174fa39db2a0dfa1218a652021-11-18T08:23:50ZHuman intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis.1932-620310.1371/journal.pone.0094381https://doaj.org/article/960d25274e174fa39db2a0dfa1218a652014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24722663/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Objectives</h4>High frequency oscillations (HFOs) have been proposed as a new biomarker for epileptogenic tissue. The exact characteristics of clinically relevant HFOs and their detection are still to be defined.<h4>Methods</h4>We propose a new method for HFO detection, which we have applied to six patient iEEGs. In a first stage, events of interest (EoIs) in the iEEG were defined by thresholds of energy and duration. To recognize HFOs among the EoIs, in a second stage the iEEG was Stockwell-transformed into the time-frequency domain, and the instantaneous power spectrum was parameterized. The parameters were optimized for HFO detection in patient 1 and tested in patients 2-5. Channels were ranked by HFO rate and those with rate above half maximum constituted the HFO area. The seizure onset zone (SOZ) served as gold standard.<h4>Results</h4>The detector distinguished HFOs from artifacts and other EEG activity such as interictal epileptiform spikes. Computation took few minutes. We found HFOs with relevant power at frequencies also below the 80-500 Hz band, which is conventionally associated with HFOs. The HFO area overlapped with the SOZ with good specificity > 90% for five patients and one patient was re-operated. The performance of the detector was compared to two well-known detectors.<h4>Conclusions</h4>Compared to methods detecting energy changes in filtered signals, our second stage - analysis in the time-frequency domain - discards spurious detections caused by artifacts or sharp epileptic activity and improves the detection of HFOs. The fast computation and reasonable accuracy hold promise for the diagnostic value of the detector.Sergey BurnosPeter HilfikerOguzkan SürücüFelix ScholkmannNiklaus KrayenbühlThomas GrunwaldJohannes SarntheinPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 4, p e94381 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sergey Burnos Peter Hilfiker Oguzkan Sürücü Felix Scholkmann Niklaus Krayenbühl Thomas Grunwald Johannes Sarnthein Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis.
description	<h4>Objectives</h4>High frequency oscillations (HFOs) have been proposed as a new biomarker for epileptogenic tissue. The exact characteristics of clinically relevant HFOs and their detection are still to be defined.<h4>Methods</h4>We propose a new method for HFO detection, which we have applied to six patient iEEGs. In a first stage, events of interest (EoIs) in the iEEG were defined by thresholds of energy and duration. To recognize HFOs among the EoIs, in a second stage the iEEG was Stockwell-transformed into the time-frequency domain, and the instantaneous power spectrum was parameterized. The parameters were optimized for HFO detection in patient 1 and tested in patients 2-5. Channels were ranked by HFO rate and those with rate above half maximum constituted the HFO area. The seizure onset zone (SOZ) served as gold standard.<h4>Results</h4>The detector distinguished HFOs from artifacts and other EEG activity such as interictal epileptiform spikes. Computation took few minutes. We found HFOs with relevant power at frequencies also below the 80-500 Hz band, which is conventionally associated with HFOs. The HFO area overlapped with the SOZ with good specificity > 90% for five patients and one patient was re-operated. The performance of the detector was compared to two well-known detectors.<h4>Conclusions</h4>Compared to methods detecting energy changes in filtered signals, our second stage - analysis in the time-frequency domain - discards spurious detections caused by artifacts or sharp epileptic activity and improves the detection of HFOs. The fast computation and reasonable accuracy hold promise for the diagnostic value of the detector.
format	article
author	Sergey Burnos Peter Hilfiker Oguzkan Sürücü Felix Scholkmann Niklaus Krayenbühl Thomas Grunwald Johannes Sarnthein
author_facet	Sergey Burnos Peter Hilfiker Oguzkan Sürücü Felix Scholkmann Niklaus Krayenbühl Thomas Grunwald Johannes Sarnthein
author_sort	Sergey Burnos
title	Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis.
title_short	Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis.
title_full	Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis.
title_fullStr	Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis.
title_full_unstemmed	Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis.
title_sort	human intracranial high frequency oscillations (hfos) detected by automatic time-frequency analysis.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/960d25274e174fa39db2a0dfa1218a65
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Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis.

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