Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings

Gardner, Alex; Worrell, Greg; Marsh, Eric D.; Dlugos, Dennis J.; Litt, Brian

doi:10.1016/j.clinph.2006.12.019

Cited by 237 publications

(247 citation statements)

References 24 publications

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“…We detect these FOs by identifying localized power increments in narrow frequency bands. In this way we have an increased sensitivity compared to existing intracranial HFO detectors (Staba et al, 2002;Crepon et al, 2010;Gardner et al, 2007;Zelmann et al, 2010) that look for a power increase in the whole frequency band of interest; the increase in power in a narrow band coincident with the FO's bandwidth is greater than the increment in a wider band. Then, the narrowbands used to detect the FOs should not be wider than the bandwidth of these FOs.…”

Section: Pre-detection Stagementioning

confidence: 99%

“…The visual detection and marking of events in these recordings by human reviewers is therefore a very time consuming task, and subjectivity in the results is unavoidable. Several automatic detectors have been proposed to aid in the detection of HFOs in intracranial recordings (Staba et al, 2002;Gardner et al, 2007;Crepon et al, 2010;Zelmann et al, 2010). We propose an algorithm specially designed to automatically detect FOs in scalp EEG recordings.…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection of fast oscillations (40–200Hz) in scalp EEG recordings

Ellenrieder

Andrade-Valença

Dubeau

et al. 2012

Clinical Neurophysiology

111

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Objective-We aim to automatically detect fast oscillations (40-200 Hz) related to epilepsy on scalp EEG recordings.Methods-The detector first finds localized increments of the signal power in narrow frequency bands. A simple classification based on two features, a narrowband to wideband signal amplitude ratio and an absolute narrowband signal amplitude, then allows for an important reduction in the number of false positives.Results-When compared to an expert, the performance in 15 focal epilepsy patients resulted in 3.6 false positives per minute at 95% sensitivity, with at least 40% of the detected events being true positives. In most of the patients the channels showing the highest number of events according to the expert and the automatic detector were the same.Conclusions-A high sensitivity is achieved with the proposed automatic detector, but results should be reviewed by an expert to remove false positives.Significance-The time required to mark fast oscillations on scalp EEG recordings is drastically reduced with the use of the proposed detector. Thus, the automatic detector is a useful tool in studies aiming to create a better understanding of the fast oscillations visible on the scalp.

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Section: Pre-detection Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic detection of fast oscillations (40–200Hz) in scalp EEG recordings

Ellenrieder

Andrade-Valença

Dubeau

et al. 2012

Clinical Neurophysiology

111

View full text Add to dashboard Cite

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“…The RMS detector is one of the most widely used automatic HFO detectors in published studies (Gardner et al 2007;Blanco et al 2010;Zelmann et al 2012;Gliske et al 2016). …”

Section: Rms Detectormentioning

confidence: 99%

“…There are several means to reduce the FDR, e.g. applying post-processing steps (Burnos et al 2014;Cho et al 2014;Amiri et al 2016;Gliske et al 2016) or using human validation (Staba et al 2002;Gardner et al 2007;Crépon et al 2010). Here we propose another approach, in which α is optimized based on FDR instead of FPR.…”

Section: Parameter Optimization To Reduce the False Detection Ratementioning

confidence: 99%

“…A majority of published detectors focus primarily on the first step of this process and rely on human visual validation for the rejection of artifacts (Staba et al 2002;Gardner et al 2007;Crépon et al 2010) or accept the results of detection without post-processing Dümpelmann et al 2012;Chaibi et al 2013). To detect high frequency events that stand out from the background, these algorithms filter the raw data in the frequency band of interest and then estimate the energy of the signal using various techniques, including the RMS amplitude (Staba et al 2002), line length (Gardner et al 2007), Hilbert transform (Crépon et al 2010), Hilbert Huang transform (Chaibi et al 2013), or a combination of techniques Dümpelmann et al 2012). Then a threshold is applied, along with other parameters such as minimum duration, minimum number of oscillations, or minimum time between successive events.…”

Section: Introductionmentioning

confidence: 99%

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

Charupanit

Lopour

2017

Brain Topogr

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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

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Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings

Abstract: This algorithm will be useful for analyzing large EEG databases to determine the pathophysiological significance of HFO events in human epileptic networks.

Cited by 237 publications

References 24 publications

Automatic detection of fast oscillations (40–200Hz) in scalp EEG recordings

Automatic detection of fast oscillations (40–200Hz) in scalp EEG recordings

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

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