Spatiotemporal patterns of high-frequency activity (80-170 Hz) in long-term intracranial EEG

Chen, Zhuying; Grayden, David B.; Burkitt, Anthony N.; Seneviratne, Udaya; D’Souza, Wendyl; French, Chris; Karoly, Philippa J.; Dell, Katrina; Cook, Mark; Maturana, Matias I.

doi:10.1101/2020.03.26.999425

Cited by 6 publications

(10 citation statements)

References 45 publications

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“…Interictalfluctuations related to epilepsy are also seen: high frequency oscillation (HFO) rates vary in location and power within each subject over time (Gliske et al, 2018). Interictal spikes also change in their location and rate over hours to days (Baud et al, 2018; Chen et al, 2021; Conrad et al, 2020; Gliske et al, 2018; Karoly et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…We expected these fluctuations to appear on the timescale of hours to days, and we investigated if they can also explain how seizure evolutions change within the same patient. Previous study suggests that many interictal features, including interictal spike rate (Baud et al, 2018; Karoly et al, 2016, 2017; Proix et al, 2021) and high frequency oscillation rate (Chen et al, 2021; Gliske et al, 2018; Scott, Gliske, Kuhlmann, & Stacey, 2021) may serve as biomarkers for modulatory processes. Here, we investigate the full spectral range, using band power in main EEG frequency bands, to capture a complete view of brain activities.…”

Section: Introductionmentioning

confidence: 99%

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Fluctuations in EEG band power at subject‐specific timescales over minutes to days explain changes in seizure evolutions

Panagiotopoulou

Papasavvas

Schroeder

et al. 2022

Human Brain Mapping

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Epilepsy is recognised as a dynamic disease, where both seizure susceptibility and seizure characteristics themselves change over time. Specifically, we recently quantified the variable electrographic spatio‐temporal seizure evolutions that exist within individual patients. This variability appears to follow subject‐specific circadian, or longer, timescale modulations. It is therefore important to know whether continuously recorded interictaliEEG features can capture signatures of these modulations over different timescales. In this study, we analyse continuous intracranial electroencephalographic (iEEG) recordings from video‐telemetry units and find fluctuations in iEEG band power over timescales ranging from minutes up to 12 days. As expected and in agreement with previous studies, we find that all subjects show a circadian fluctuation in their iEEG band power. We additionally detect other fluctuations of similar magnitude on subject‐specific timescales. Importantly, we find that a combination of these fluctuations on different timescales can explain changes in seizure evolutions in most subjects above chance level. These results suggest that subject‐specific fluctuations in iEEG band power over timescales of minutes to days may serve as markers of seizure modulating processes. We hope that future study can link these detected fluctuations to their biological driver(s). There is a critical need to better understand seizure modulating processes, as this will enable the development of novel treatment strategies that could minimise the seizure spread, duration or severity and therefore the clinical impact of seizures.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fluctuations in EEG band power at subject‐specific timescales over minutes to days explain changes in seizure evolutions

Panagiotopoulou

Papasavvas

Schroeder

et al. 2022

Human Brain Mapping

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“…However, short-term monitoring has major limitations. The spatiotemporal organization of interictal EA, including epileptiform spikes and high frequency oscillations (HFOs), changes over long time scales (months to years), so short-term capture of interictal EA is unreliable (13,14). In addition, seizure rates show high natural variability and require long-term recording to identify clinically relevant improvements (15,16).…”

Section: Introductionmentioning

confidence: 99%

Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System

et al. 2021

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Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder®), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.

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“…24 HFA, defined as high-frequency (80-170 Hz) events with amplitude significantly higher than the background EEG, was previously automatically detected over the entire iEEG recordings and manually verified by 3 neurologists. 23 To account for the initial high postimplantation variability in HFA rates, 23 this study used only data later than 100 days after implantation for seizure forecasting.…”

Section: Data Sourcementioning

confidence: 99%

Seizure Forecasting by High-Frequency Activity (80–170 Hz) in Long-term Continuous Intracranial EEG Recordings

et al. 2022

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Background and Objectives:Reliable seizure forecasting has important implications in epilepsy treatment and improving the quality of lives for people with epilepsy. High-frequency activity (HFA) is one biomarker that has received significant attention over the past two decades, but its predictive value in seizure forecasting remains uncertain. This work aimed to determine the utility of HFA in seizure forecasting.Methods:We used seizure data and HFA (80-170 Hz) data obtained from long-term, continuous intracranial EEG recordings of drug-resistant epilepsy patients. Instantaneous rates and phases of HFA cycles were used as features for seizure forecasting. Seizure forecasts based on each individual HFA feature, and using a combined approach, were generated pseudo-prospectively (causally). To compute the instantaneous phases for pseudo-prospective forecasting, real-time phase estimation based on an autoregressive model was employed. Features were combined using a weighted average approach. The performance of seizure forecasting was primarily evaluated by the area under the curve (AUC).Results:Of 15 studied patients (median recording duration: 557 days, median seizures: 151), 12 patients with more than 10 seizures after 100 recording days were included in the pseudo-prospective analysis. The presented real-time phase estimation is feasible and can causally estimate the instantaneous phases of HFA cycles with high accuracy. Pseudo-prospective seizure forecasting based on HFA rates and phases performed significantly better than chance in 11 out of 12 patients, although there were patient-specific differences. Combining rate and phase information improved forecasting performance compared to using either feature alone. The combined forecast using the best-performing channel yielded a median AUC of 0.70, a median sensitivity of 0.57, and a median specificity of 0.77.Conclusions:These findings show that HFA could be useful for seizure forecasting and represent proof of concept for utilizing prior information of patient-specific relationships between HFA and seizures in pseudo-prospective forecasting. Future seizure forecasting algorithms might benefit from the inclusion of HFA, and the real-time phase estimation approach can be extended to other biomarkers.Classification of Evidence:This study provides Class IV evidence that high-frequency activity (80-170 Hz) in long-term continuous intracranial EEG can be useful to forecast seizures in patients with refractory epilepsy.

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Spatiotemporal patterns of high-frequency activity (80-170 Hz) in long-term intracranial EEG

Cited by 6 publications

References 45 publications

Fluctuations in EEG band power at subject‐specific timescales over minutes to days explain changes in seizure evolutions

Fluctuations in EEG band power at subject‐specific timescales over minutes to days explain changes in seizure evolutions

Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System

Seizure Forecasting by High-Frequency Activity (80–170 Hz) in Long-term Continuous Intracranial EEG Recordings

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