230 days of ultra long‐term subcutaneous EEG: seizure cycle analysis and comparison to patient diary

Viana, Pedro; Duun‐Henriksen, Jonas; Glasstëter, Martin; Dümpelmann, Matthias; Nurse, Ewan S.; Martins, Isabel Pavão; Dumanis, Sonya B.; Schulze‐Bonhage, Andreas; Freestone, Dean R.; Brinkmann, Benjamin H.; Richardson, Mark

doi:10.1002/acn3.51261

Cited by 56 publications

(64 citation statements)

References 16 publications

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“…Second, subjects’ behavior might change throughout the time of the study, with subjects becoming more active after having recovered from the implantation procedure. Third, long‐term cyclical changes in brain activity have been described; in particular, spike rates and seizure rates have been shown to vary both at circadian but also at multi‐day timescales 32–34 . This could also be reflected in frequency band activity, or indirectly in patients’ behavior.…”

Section: Discussionmentioning

confidence: 99%

Signal quality and power spectrum analysis of remote ultra long‐term subcutaneous EEG

Viana

Remvig²,

Duun‐Henriksen³

et al. 2021

Epilepsia

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Objective Ultra long‐term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long‐term quality and consistency of the sqEEG signal, which is the objective of this study. Methods The largest multicenter cohort of sqEEG was analyzed, including 14 patients with epilepsy and 12 healthy subjects, implanted with a sqEEG device (24/7 EEG™ SubQ), and recorded from 23 to 230 days (median 42 days), with a median data capture rate of 75% (17.9 hours/day). Median power spectral density plots of each subject were examined for physiological peaks, including at diurnal and nocturnal periods. Long‐term temporal trends in signal impedance and power spectral features were investigated with subject‐specific linear regression models and group‐level linear mixed‐effects models. Results sqEEG spectrograms showed an approximate 1/f power distribution. Diurnal peaks in the alpha range (8‐13Hz) and nocturnal peaks in the sigma range (12‐16Hz) were seen in the majority of subjects. Signal impedances remained low, and frequency band powers were highly stable throughout the recording periods. Significance The spectral characteristics of minimally invasive, ultra long‐term sqEEG are similar to scalp EEG, whereas the signal is highly stationary. Our findings reinforce the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain‐computer interfaces.

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Section: Discussionmentioning

confidence: 99%

Signal quality and power spectrum analysis of remote ultra long‐term subcutaneous EEG

Viana

Remvig²,

Duun‐Henriksen³

et al. 2021

Epilepsia

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“…For instance average rates of interictal epileptic activity ( 4 ), the variance and autocorrelation of EEG ( 15 ), and even average heart rate ( 21 ) all show multiday cycles that are more robustly predictive of seizure likelihood than looking at past seizure times. Additionally, a recent comparison of seizure diaries and epileptic activity captured from chronic sub-scalp recording systems ( 22 ) showed discrepancies between the cyclic distributions of self-reported and electrographic events, even when diaries were relatively accurate ( 23 ). This is perhaps not unexpected given many seizures are not recognised by patients, particularly those occurring in sleep.…”

Section: Misconception 1: Seizure Diaries Are Too Noisy To Infer Cyclesmentioning

confidence: 99%

“…Although overlapping symptomatically, this fundamental difference in generalised seizure generation may mean that electrographic multiday cycles are not present in generalised epilepsies due to the difference in the seizure generation process. We anticipate that the next generation of chronic EEG devices ( 23 , 28 , 63 ) will be able to elucidate any such cycles as they have for focal epilepsies.…”

Section: Misconception 5: Cycles Are Only Relevant For Focal Epilepsiesmentioning

confidence: 99%

Epileptic Seizure Cycles: Six Common Clinical Misconceptions

Karoly

Freestone²,

Eden³

et al. 2021

Front. Neurol.

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“…Invasive intracranial systems, such as the RNS System (NeuroPace) and the Percept PC (Medtronic), are available but are built for neurostimulation, do not store sufficient data and are too invasive for diagnostic applications (19). Alternatively, sub-scalp EEG systems are minimally-invasive tools that may address the need for objective ultra-long term EEG recordings (19,20), allowing for personalised and accurate epilepsy management.…”

Section: Introductionmentioning

confidence: 99%

“…This case study demonstrates how cycles can be derived from events in the EEG and in turn, these cycles can be used to forecast epileptic seizures. The forecasting method has been built on previous work in seizure cycles (20,(29)(30)(31) and interictal EA cycles (17,18,32).…”

Section: Introductionmentioning

confidence: 99%

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

Stirling

Karoly

Maturana³

et al. 2021

Preprint

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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 TM), 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 utilising 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 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) is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.

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230 days of ultra long‐term subcutaneous EEG: seizure cycle analysis and comparison to patient diary

Cited by 56 publications

References 16 publications

Signal quality and power spectrum analysis of remote ultra long‐term subcutaneous EEG

Signal quality and power spectrum analysis of remote ultra long‐term subcutaneous EEG

Epileptic Seizure Cycles: Six Common Clinical Misconceptions

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

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