Focal resection of fast ripples on extraoperative intracranial EEG improves seizure outcome in pediatric epilepsy

Akiyama, Tomoyuki; McCoy, Bláthnaid; Go, Cristina; Ochi, Ayako; Elliott, Irene; Akiyama, Mari; Donner, Elizabeth; Weiss, Shelly K.; Snead, O. Carter; Rutka, James T.; Drake, James M.; Otsubo, Hiroshi

doi:10.1111/j.1528-1167.2011.03199.x

Cited by 253 publications

(263 citation statements)

References 31 publications

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“…HFOs are defined as spontaneous EEG patterns in the frequency range between 80-500 Hz, consisting of at least four oscillations that clearly stand out of the background activity . Interictal HFOs proved to be more specific in localizing the SOZ than spikes (Jacobs et al, 2008) and have shown a good correlation with the postsurgical outcome in epilepsy patients (Akiyama et al, 2011;Jacobs et al, 2010;van 't Klooster et al, 2015;Wu et al, 2010).…”

Section: Introductionmentioning

confidence: 90%

“…FRs are considered more specific for epileptogenicity because of their close relation to the SOZ (Engel et al, 2009). Ripples were shown to be a less specific marker of the epileptogenic zone than FRs regarding the surgical outcome (Akiyama et al, 2011;van 't Klooster et al, 2015). This is best explained by the fact that there are not only epileptic and hence pathological ripples, but also spontaneous physiological ripples generated by the healthy brain.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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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“…Subsequent studies have demonstrated that HFOs can be reliably recorded using commercially-available macroelectrodes, both subdural and depth electrodes, with the highest rates during slow-wave NREM sleep. They were found to be an independent epilepsy marker both in neocortical epilepsy and mTLE [17][18][19][20][21][22]. The regions showing high rates of interictal or ictal HFOs spatially colocalize with the SOZ or EZ, and resection of areas with higher interictal or ictal HFO rates have been significantly associated with a better surgical outcome (Table 1).…”

Section: High-frequency Oscillationsmentioning

confidence: 99%

Advances of Intracranial Electroencephalography in Localizing the Epileptogenic Zone

Jin

Wang

2016

Neurosci. Bull.

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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.

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“…1) Based on the literature, we assume that a high rate of pathological HFOs is a reliable marker of the epileptogenic zone [4,19,30,35,36]. We assume this is also the case for patients with very different types of epilepsy.…”

Section: Reliability Of Detection Of the Epileptogenic Zonementioning

confidence: 99%

“…Since visual marking of HFOs is highly time-consuming, several algorithms for automatic or semi-automatic detection of HFOs have been proposed [11][12][13][14][15][16][17][18][19]. While earlier detectors rely rather on thresholds in the time domain, a number of recent detectors also incorporate the frequency domain, which is computationally more demanding.…”

Section: Introductionmentioning

confidence: 99%

Human Intracranial High Frequency Oscillations (HFOs) Detected by Automatic Time-Frequency Analysis

Burnos¹,

Hilfiker²,

Sürücü³

et al. 2014

J Neurol Surg A Cent Eur Neurosurg

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Objectives: 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. Methods: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. Results: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.Conclusions: Compared to methods detecting energy changes in filtered signals, our second stage -analysis in the timefrequency 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.

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Focal resection of fast ripples on extraoperative intracranial EEG improves seizure outcome in pediatric epilepsy

Cited by 253 publications

References 31 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

Advances of Intracranial Electroencephalography in Localizing the Epileptogenic Zone

Human Intracranial High Frequency Oscillations (HFOs) Detected by Automatic Time-Frequency Analysis

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