High-frequency oscillations (HFOs) in clinical epilepsy

Jacobs, Julia; Staba, Richard J.; Asano, Eishi; Otsubo, Hiroshi; Wu, Jianping; Zijlmans, Maeike; Mohamed, Ismail; Kahane, Philippe; Dubeau, François; Navarro, Vincent; Gotman, Jean

doi:10.1016/j.pneurobio.2012.03.001

Cited by 373 publications

(382 citation statements)

References 103 publications

Supporting

Mentioning

349

Contrasting

Unclassified

Order By: Relevance

“…In that study we used ordinal pattern analysis in a smaller group of patients without assessing the relationship between surgically removed brain regions and post-operative seizure freedom. Most signals with maximal time-irreversibility are relatively slow and therefore much smaller sampling rates could be used compared to, for example, high frequency oscillations as markers of epileptogenicity (Modur et al, 2011, Jacobs et al, 2012.…”

Section: Discussionmentioning

confidence: 99%

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone

Schindler

Rummel

Andrzejak

et al. 2016

Clinical Neurophysiology

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone

Schindler

Rummel

Andrzejak

et al. 2016

Clinical Neurophysiology

View full text Add to dashboard Cite

“…It is difficult to differentiate physiological ripples from pathological ripples by rate, duration, and amplitude because they overlap greatly in each parameter [30,40,41,54,55]. The morphology of HFOs does not improve delineation of the EZ either [56].…”

Section: High-frequency Oscillationsmentioning

confidence: 99%

Advances of Intracranial Electroencephalography in Localizing the Epileptogenic Zone

Jin

Wang

2016

Neurosci. Bull.

View full text Add to dashboard Cite

Intracranial electroencephalography (iEEG) provides the best precision in estimating the location and boundary of an epileptogenic zone. Analysis of iEEG in the routine EEG frequency range (0.5-70 Hz) remains the basis in clinical practice. Low-voltage fast activity is the most commonly reported ictal onset pattern in neocortical epilepsy, and low-frequency high-amplitude repetitive spiking is the most commonly reported ictal onset pattern in mesial temporal lobe epilepsy. Recent studies using wideband EEG recording have demonstrated that examining higher (80-1000 Hz) and lower (0.016-0.5 Hz) EEG frequencies can provide additional diagnostic information and help to improve the surgical outcome. In addition, novel computational techniques of iEEG signal analysis have provided new insights into the epileptic network. Here, we review some of these recent advances. Although these sophisticated and advanced techniques of iEEG analysis show promise in localizing the epileptogenic zone, their utility needs to be further validated in larger studies.

show abstract

“…Pathological HFOs are broadly defined as spontaneous electrographic events of at least three to four full oscillations that are clearly distinguishable from the background signal, with frequencies ranging from 80-500 Hz (Buzsáki et al 1992;Jacobs et al 2012). Visual identification by expert reviewers is a widely used procedure for detecting HFOs in both scalp and intracranial EEG recordings (Jacobs et al 2014;Ferrari-Marinho et al 2015), and it is currently considered the gold standard (Worrell et al 2012).…”

Section: Introductionmentioning

confidence: 99%

A Simple Statistical Method for the Automatic Detection of Ripples in Human Intracranial EEG

Charupanit

Lopour

2017

Brain Topogr

View full text Add to dashboard Cite

Keywords:HFO Epilepsy Ripple Electrocorticogram Depth electrode Highlights  New automatic HFO detection algorithm requires optimization of only one tunable parameter that is related to the number of allowable false positive events.  The detector uses an iterative estimate of the background amplitude distribution to choose the threshold.  Cross-validation tests indicate that sensitivity is superior to a commonly used automated detector based on RMS amplitude. AbstractHigh frequency oscillations (HFOs) are a promising biomarker of epileptic tissue, but detection of these electrographic events remains a challenge. Automatic detectors show encouraging results, but they typically require optimization of multiple parameters, which is a barrier to good performance and broad applicability. We therefore propose a new automatic HFO detection algorithm, focusing on simplicity and ease of implementation. It requires tuning of only an amplitude threshold, which can be determined by an iterative process or directly calculated from statistics of the rectified filtered data (i.e. mean plus standard deviation). The iterative approach uses an estimate of the amplitude probability distribution of the background activity to calculate the optimum threshold for identification of transient high amplitude events. We tested both the iterative and non-iterative approaches using a dataset of visually marked HFOs, and we compared the performance to a commonly used detector based on the root-mean-square. When the threshold was optimized for individual channels via ROC curve, all three methods were comparable. The iterative detector achieved a sensitivity of 99.6%, false positive rate (FPR) of 1.1%, and false detection rate (FDR) of 37.3%. However, in an eight-fold cross-validation test, the iterative method had better sensitivity than the other two methods (80.0% compared to 64.4% and 65.8%), with FPR and FDR of 1.3%, and 49.4%, respectively. The simplicity of this algorithm, with only a single parameter, will enable consistent application of automatic detection across research centers and recording modalities, and it may therefore be a powerful tool for the assessment and localization of epileptic activity.2

show abstract

High-frequency oscillations (HFOs) in clinical epilepsy

Cited by 373 publications

References 103 publications

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone

Advances of Intracranial Electroencephalography in Localizing the Epileptogenic Zone

A Simple Statistical Method for the Automatic Detection of Ripples in Human Intracranial EEG

Contact Info

Product

Resources

About