Interrater reliability of visually evaluated high frequency oscillations

Spring, Aaron M.; Pittman, Daniel J.; Aghakhani, Yahya; Jirsch, Jeffrey; Pillay, Neelan; Bello-Espinosa, Luis; Josephson, Colin B.; Federico, Paolo

doi:10.1016/j.clinph.2016.12.017

Cited by 66 publications

(78 citation statements)

References 41 publications

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“…To the extent that the report of Spring and colleagues threatens the utility of HFOs in clinical and research arenas, we believe that this study may provide some reassurance that the IRR of visual HFO identification is adequate among highly experienced electroencephalographers. Nevertheless, IRR poses a continued challenge, and is especially important in consideration of the multicenter clinical trials presently underway, which seek to establish the significance and prognostic value of HFOs in the surgical management of epilepsy.…”

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

“…In the only study to evaluate the interrater reliability (IRR) of HFO identification using ECoG data, the authors reported poor IRR, at least among a team with varied experience in HFO analysis. 14 As such, the clinical utility of HFOs is presently limited by the lack of a standardized approach to identification and quantification. Accordingly, we set out to evaluate the IRR of both HFOs as well as conventional epileptiform discharges (e.g., spikes), on both ECoG and scalp EEG, by electroencephalographers who are experienced in the identification and quantification of HFOs.…”

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confidence: 99%

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Interrater reliability in visual identification of interictal high‐frequency oscillations on electrocorticography and scalp EEG

Nariai

Bernardo

et al. 2018

Epilepsia Open

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SummaryHigh‐frequency oscillations (HFOs), including ripples (Rs) and fast ripples (FRs), are promising biomarkers of epileptogenesis, but their clinical utility is limited by the lack of a standardized approach to identification. We set out to determine whether electroencephalographers experienced in HFO analysis can reliably identify and quantify interictal HFOs. Two blinded raters independently reviewed 10 intraoperative electrocorticography (ECoG) samples from epilepsy surgery cases, and 10 scalp EEG samples from epilepsy monitoring unit evaluations. HFOs were visually marked using bandpass filters (R, 80–250 Hz; FR, 250–500 Hz) with a sampling frequency of 2,000 Hz. There was agreement as to the presence or absence of epileptiform discharges (EDs), Rs, and FRs, in 17, 18, and 18 cases, respectively. Interrater reliability (IRR) was favorable with κ = 0.70, 0.80, and 0.80, respectively, and similar for ECoG and scalp electroencephalography (EEG). Furthermore, interclass correlation for rates of Rs (0.99, 95% confidence interval [CI] 0.96–0.99) and FRs (0.77, 95% CI 0.41–0.91) were superior in comparison to EDs (0.37, 95% CI −0.60 to 0.75). Our data suggest that HFO identification and quantification are reliable among experienced electroencephalographers. Our findings support the reliability of utilizing HFO data in both research and clinical arenas.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Interrater reliability in visual identification of interictal high‐frequency oscillations on electrocorticography and scalp EEG

Nariai

Bernardo

et al. 2018

Epilepsia Open

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“…A visual review of HFOs is time-consuming and suffers from reviewer bias, resulting in poor inter-reviewer agreement [23]. This has led to the development of several automated HFOs detectors (Fig.…”

Section: Hfos Detectionmentioning

confidence: 99%

Progress and remaining challenges in the application of high frequency oscillations as biomarkers of epileptic brain

Khadjevand

Cimbálník

Worrell

2017

Current Opinion in Biomedical Engineering

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High-frequency oscillations (HFOs: 100 – 600 Hz) have been widely proposed as biomarkers of epileptic brain tissue. In addition, HFOs over a broader range of frequencies spanning 30 – 2000 Hz are potential biomarkers of both physiological and pathological brain processes. The majority of the results from humans with focal epilepsy have focused on HFOs recorded directly from the brain with intracranial EEG (iEEG) in the high gamma (65 – 100 Hz), ripple (100 – 250 Hz), and fast ripple (250 – 600 Hz) frequency ranges. These results are supplemented by reports of HFOs recorded with iEEG in the low gamma (30 – 65Hz) and very high frequency (500 – 2000 Hz) ranges. Visual detection of HFOs is laborious and limited by poor inter-rater agreement; and the need for accurate, reproducible automated HFOs detection is well recognized. In particular, the clinical translation of HFOs as a biomarker of the epileptogenic brain has been limited by the ability to reliably detect and accurately classify HFOs as physiological or pathological. Despite these challenges, there has been significant progress in the field, which is the subject of this review. Furthermore, we provide data and corresponding analytic code in an effort to promote reproducible research and accelerate clinical translation.

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“…One barrier to utilizing HFOs for clinical decision making is that inter-reader agreement on what constitutes an event is often poor (Spring et al, 2017). Therefore, it is difficult to validate automated HFO detectors.…”

Section: Introductionmentioning

confidence: 99%

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

Shimamoto

Waldman

Orosz

et al. 2018

Clinical Neurophysiology

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Objective To develop and validate a detector that identifies ripple (80–200 Hz) events in intracranial EEG (iEEG) recordings in a referential montage and utilizes independent component analysis (ICA) to eliminate or reduce high-frequency artifact contamination. Also, investigate the correspondence of detected ripples and the seizure onset zone (SOZ). Methods iEEG recordings from 16 patients were first band-pass filtered (80–600 Hz) and Infomax ICA was next applied to derive the first independent component (IC1). IC1 was subsequently pruned, and an artifact index was derived to reduce the identification of high-frequency events introduced by the reference electrode signal. A Hilbert detector identified ripple events in the processed iEEG recordings using amplitude and duration criteria. The identified ripple events were further classified and characterized as true or false ripple on spikes, or ripples on oscillations by utilizing a topographical analysis to their time-frequency plot, and confirmed by visual inspection. Results The signal to noise ratio was improved by pruning IC1. The precision of the detector for ripple events was 91.27 ± 4.3%, and the sensitivity of the detector was 79.4 ± 3.0% (N = 16 patients, 5842 ripple events). The sensitivity and precision of the detector was equivalent in iEEG recordings obtained during sleep or intra-operatively. Across all the patients, true ripple on spike rates and also the rates of false ripple on spikes, that were generated due to filter ringing, classified the seizure onset zone (SOZ) with an area under the receiver operating curve (AUROC) of >76%. The magnitude and spectral content of true ripple on spikes generated in the SOZ was distinct as compared with the ripples generated in the NSOZ (p < .001). Conclusions Utilizing ICA to analyze iEEG recordings in referential montage provides many benefits to the study of high-frequency oscillations. The ripple rates and properties defined using this approach may accurately delineate the seizure onset zone. Significance Strategies to improve the spatial resolution of intracranial EEG and reduce artifact can help improve the clinical utility of HFO biomarkers.

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Interrater reliability of visually evaluated high frequency oscillations

Cited by 66 publications

References 41 publications

Interrater reliability in visual identification of interictal high‐frequency oscillations on electrocorticography and scalp EEG

Interrater reliability in visual identification of interictal high‐frequency oscillations on electrocorticography and scalp EEG

Progress and remaining challenges in the application of high frequency oscillations as biomarkers of epileptic brain

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

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