High Frequency Oscillations and spikes: Separating real HFOs from false oscillations

Amiri, Mina; Lina, Jean‐Marc; Pizzo, Francesca; Gotman, Jean

doi:10.1016/j.clinph.2015.04.290

Cited by 99 publications

(110 citation statements)

References 23 publications

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“…The visual and automatic detection of this HFO type is challenging, because its irregular amplitude could be either an effect of filtering sharp epileptiform spikes ("false ripple", (Benar et al, 2010)) or a ripple superimposed on a sharp spike (Amiri et al, 2015). We showed that type 1 and type 2 HFO rates are highly correlated ( Figure 3) and reflect epileptogenic activity equally well.…”

Section: Properties Of the Four Hfo Typesmentioning

confidence: 88%

The morphology of high frequency oscillations (HFO) does not improve delineating the epileptogenic zone

Burnos

Frauscher

Zelmann

et al. 2016

Clinical Neurophysiology

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OBJECTIVE: We hypothesized that high frequency oscillations (HFOs) with irregular amplitude and frequency more specifically reflect epileptogenicity than HFOs with stable amplitude and frequency. METHODS: We developed a fully automatic algorithm to detect HFOs and classify them based on their morphology, with types defined according to regularity in amplitude and frequency: type 1 with regular amplitude and frequency; type 2 with irregular amplitude, which could result from filtering of sharp spikes; type 3 with irregular frequency; and type 4 with irregular amplitude and frequency. We investigated the association of different HFO types with the seizure onset zone (SOZ), resected area and surgical outcome. RESULTS: HFO rates of all types were significantly higher inside the SOZ than outside. HFO types 1 and 2 were strongly correlated to each other and showed the highest rates among all HFOs. Their occurrence was highly associated with the SOZ, resected area and surgical outcome. The automatic detection emulated visual markings with 93% true positives and 57% false detections. CONCLUSIONS: HFO types 1 and 2 similarly reflect epileptogenicity. SIGNIFICANCE: For clinical application, it may not be necessary to separate real HFOs from "false oscillations" produced by the filter effect of sharp spikes. Also for automatically detected HFOs, surgical outcome is better when locations with higher HFO rates are included in the resection. We hypothesized that high frequency oscillations (HFOs) with irregular amplitude and frequency more specifically reflect epileptogenicity than HFOs with stable amplitude and frequency. Methods:We developed a fully automatic algorithm to detect HFOs and classify them based on their morphology, with types defined according to regularity in amplitude and frequency: type 1 with regular amplitude and frequency; type 2 with irregular amplitude, which could result from filtering of sharp spikes; type 3 with irregular frequency; and type 4 with irregular amplitude and frequency. We investigated the association of different HFO types with the seizure onset zone (SOZ), resected area and surgical outcome. Results:HFO rates of all types were significantly higher inside the SOZ than outside. HFO types 1 and 2 were strongly correlated to each other and showed the highest rates among all HFOs. Their occurrence was highly associated with the SOZ, resected area and surgical outcome. The automatic detection emulated visual markings with 93% true positives and 57% false detections. Conclusions:HFO types 1 and 2 similarly reflect epileptogenicity. Significance:For clinical application, it may not be necessary to separate real HFOs from "false oscillations" produced by the filter effect of sharp spikes. Also for automatically detected HFOs, surgical outcome is better when locations with higher HFO rates are included in the resection.

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Section: Properties Of the Four Hfo Typesmentioning

confidence: 88%

The morphology of high frequency oscillations (HFO) does not improve delineating the epileptogenic zone

Burnos

Frauscher

Zelmann

et al. 2016

Clinical Neurophysiology

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“…Another option to reduce the FDR and detection of artifacts is to apply a post-processing step to eliminate falsely detected events and leave only "true" HFOs. This can be done either automatically, using an artifact rejection algorithm (Burnos et al 2014;Cho et al 2014;Amiri et al 2016;Gliske et al 2016) or data classification via clustering (Blanco et al 2010;Malinowska et al 2015), or manually with supervision by experts.…”

Section: Discussionmentioning

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%

“…More recently, several algorithms for automated artifact rejection have been developed (Burnos et al 2014;Cho et al 2014;Amiri et al 2016;Gliske et al 2016). These algorithms are independent from the initial detection, which is accomplished through either human visual analysis (Amiri et al 2016) or an existing detector (Burnos et al 2014;Cho et al 2014;Gliske et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms are independent from the initial detection, which is accomplished through either human visual analysis (Amiri et al 2016) or an existing detector (Burnos et al 2014;Cho et al 2014;Gliske et al 2016). These methods reduce the number of false detections, but again increase the complexity and number of parameters, making implementation more challenging.…”

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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High‐frequency oscillations are not better biomarkers of epileptogenic tissues than spikes

Roehri

Pizzo

Lagarde

et al. 2018

Annals of Neurology

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175

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High Frequency Oscillations and spikes: Separating real HFOs from false oscillations

Cited by 99 publications

References 23 publications

The morphology of high frequency oscillations (HFO) does not improve delineating the epileptogenic zone

The morphology of high frequency oscillations (HFO) does not improve delineating the epileptogenic zone

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

High‐frequency oscillations are not better biomarkers of epileptogenic tissues than spikes

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