Seizure likelihood varies with day-to-day variations in sleep duration in patients with refractory focal epilepsy: A longitudinal EEG investigation

Kl, Dell; De, Payne; Kremen, Václav; Mi, Maturana; Gerla,; Nejedlý, Petr; Ga, Worrell; Lhotská, Lenka; Mívalt, Filip; Boston, RC; Bh, Brinkmann; D’Souza, Wendyl; An, Burkitt; Grayden, DB; Kuhlmann, Levin; Dr, Freestone; Mj, Cook

doi:10.1101/2021.05.03.21256436

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Conversely, the treatment of seizures and seizures themselves can disrupt normal sleep patterns (40,41). Sub-scalp devices provide an opportunity to investigate the complex relationship between sleep and seizures and can aid in patient management and seizure forecasting (39). The sub-scalp EEG was less noisy compared to scalp EEG.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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Electrical Brain Stimulation and Continuous Behavioral State Tracking in Ambulatory Humans

Mívalt

Kremen

Sladky

et al. 2021

Preprint

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Electrical brain stimulation (EBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy and EBS therapies may actually further disturb normal sleep patterns and sleep quality. Novel devices capable of EBS and continuous intracranial EEG (iEEG) telemetry enable detailed assessments of therapy efficacy and tracking of sleep and comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT). The iEEG recorded from HPC are used to classify sleep during concurent ANT stimulation. Simultaneous polysomnography and HPC sensing was were used to train, validate and test an automated classifier for a range of ANT EBS frequencies (2 Hz, 7Hz, 100Hz, and 145 Hz). We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC sensing channel. The patient specific classifiers performed well under all thalamic EBS frequencies with an average F1-score 0.894, and provided viable classification into major sleep categories (Awake, NREM, REM). Within this project, we retrospectively analyzed classification performance with gold-standard polysomnography annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment. The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing EBS for epilepsy and associated sleep, cognitive and mood comorbidities.

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Seizure likelihood varies with day-to-day variations in sleep duration in patients with refractory focal epilepsy: A longitudinal EEG investigation

Cited by 2 publications

References 34 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

Electrical Brain Stimulation and Continuous Behavioral State Tracking in Ambulatory Humans

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