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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Main Authors: Sergey Burnos, Peter Hilfiker, Oguzkan Sürücü, Felix Scholkmann, Niklaus Krayenbühl, Thomas Grunwald, Johannes Sarnthein
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24722663/pdf/?tool=EBI
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spelling doaj-960d25274e174fa39db2a0dfa1218a652021-03-04T09:34:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0194e9438110.1371/journal.pone.0094381Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis.Sergey BurnosPeter HilfikerOguzkan SürücüFelix ScholkmannNiklaus KrayenbühlThomas GrunwaldJohannes Sarnthein<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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24722663/pdf/?tool=EBI
collection DOAJ
language English
format Article
sources DOAJ
author Sergey Burnos
Peter Hilfiker
Oguzkan Sürücü
Felix Scholkmann
Niklaus Krayenbühl
Thomas Grunwald
Johannes Sarnthein
spellingShingle 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.
PLoS ONE
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)
series PLoS ONE
issn 1932-6203
publishDate 2014-01-01
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.
url https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24722663/pdf/?tool=EBI
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