Cycles of self-reported seizure likelihood correspond to yield of diagnostic epilepsy monitoring

Karoly, Philippa J.; Eden, Dominique; Nurse, Ewan S.; Cook, Mark; Taylor, Janelle; Dumanis, Sonya B.; Richardson, Mark; Brinkmann, Benjamin H.; Freestone, Dean R.

doi:10.1101/2020.10.05.20207407

Cited by 11 publications

(16 citation statements)

References 44 publications

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“…Hence, patient seizure diaries will remain a useful tool in clinical settings, and non-invasive forecasting systems based on mobile and wearable devices are desired by the epilepsy community ( 43 , 44 ). Wearable sensors and non-invasive features may be useful to forecast seizure likelihood ( 25 , 45 , 46 ), and self-reported events and biomarkers derived from wearables also demonstrate cycles that are co-modulated with seizure likelihood ( 30 , 38 , 40 ). However, the correlation between self-reported events and electrographic events is patient-specific.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…Cycles in seizure times were detected using a similar approach to our previous work ( 29 , 38 ). We assessed the phase locking of seizure times to a range of possible cycles using both the Omnibus test ( p < 0.05) and the synchronization index (SI ≥ 0.4) value to quantify phase locking.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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“…Recent studies have demonstrated impressive seizure forecasting performance using multiday cycles measured from implantable EEG (114,115), although prospective validation is needed. Cycles of seizure likelihood can also be measured from selfreported seizure times (116), and for a subset of people, cycles measured from seizure diaries are predictive of the likelihood of electrographic seizures and epileptic activity (116,117). Machine learning can also be used with historic trends from self-reported seizure diaries, which may be useful to forecast future reported seizures (118,119).…”

Section: Patterns and Rhythms In Seizure Probabilitymentioning

confidence: 99%

“…Both cyclic and machinelearning approaches have been shown to accurately forecast seizures (or, more specifically, seizure diary events) in both focal and generalized epilepsies. Despite inaccuracy in individual seizure reporting, long-term patterns and cycles may still be accurately inferred for many individuals (116,117). Due to the indications for use of who can have implanted EEG for long-term recording, the existence of cycles has largely been validated electrographically in individuals with focal epilepsies.…”

Section: Patterns and Rhythms In Seizure Probabilitymentioning

confidence: 99%

Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic

et al. 2021

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It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic–clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.

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“…Cycles in seizure times were detected using a similar approach to our previous work (29,38). We assessed the phase locking of seizure times to a range of possible cycles using both the Omnibus test (p < 0.05) and the synchronisation index (SI ≥ 0.4) value to quantify phase locking.…”

Section: Methodsmentioning

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.

show abstract

Cycles of self-reported seizure likelihood correspond to yield of diagnostic epilepsy monitoring

Cited by 11 publications

References 44 publications

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

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

Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic

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

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