Multiday Cycles of Heart Rate Modulate Seizure Likelihood at Daily, Weekly and Monthly Timescales: An Observational Cohort Study

Karoly, Philippa J.; Stirling, Rachel E; Freestone, Dean R.; Nurse, Ewan S.; Doyle, Brendan; Halliday, Amy J.; Neal, Andrew; Xiong, Wenjuan; Kameneva, Tatiana; Gregg, Nicholas M.; Brinkmann, Benjamin H.; Richardson, Mark; Dumanis, Sonya B.; Gerche, André La; Grayden, David B.; D’Souza, Wendyl; Cook, Mark

doi:10.2139/ssrn.3745167

Cited by 6 publications

(16 citation statements)

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“…In the current work, there was clear rhythmicity in the detected events ( Figure 3A ), which is concordant with previous work with invasive EEG showing the prevalence of circadian and multiday cycles in interictal EA ( 17 , 18 ) and seizures ( 20 , 29 , 31 ). Using a similar approach to previous work ( 20 , 40 ), cycles were detected at circadian and multiday periodicities for one individual ( Figure 3B ), with 18-day and 29-day cycles in the detected events showing the strongest relationships with seizure timing ( Figures 3C,D ). Interestingly, multiday cyclesin this subject were stronger than the circadian rhythm.…”

Section: Discussionmentioning

confidence: 74%

“…Lack of sleep and deviations from normal sleep patterns are known risk factors for seizures. 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 ).…”

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: 74%

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…On the other hand, advances in chronic EEG recording have shown that seizure cycles are more reliably estimated by continuously monitoring biomarkers of excitability ( 5 ). 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 ).…”

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

confidence: 99%

“…It has been hypothesised that endogenous weekly cycles do influence mammalian physiology, with evidence of such cycles affecting the cardio-respiratory, immune, and endocrine systems ( 34 ). A recent study found people with epilepsy showed weekly fluctuations in resting heart rate that were related to their seizure occurrence ( 21 ). Similar to epileptic activity, heart rate cycles were found at patient-specific periods of between 5 and 9 days and not necessarily linked to weekdays.…”

Section: Misconception 2: Weekly Cycles Are Driven By Weekday Behavioursmentioning

confidence: 99%

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Epileptic Seizure Cycles: Six Common Clinical Misconceptions

Karoly

Freestone²,

Eden³

et al. 2021

Front. Neurol.

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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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Multiday Cycles of Heart Rate Modulate Seizure Likelihood at Daily, Weekly and Monthly Timescales: An Observational Cohort Study

Cited by 6 publications

References 0 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

Epileptic Seizure Cycles: Six Common Clinical Misconceptions

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

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