Forecasting cycles of seizure likelihood

Karoly, Philippa J.; Cook, Mark; Maturana, Matias I.; Nurse, Ewan S.; Payne, Daniel E.; Brinkmann, Benjamin H.; Grayden, David B.; Dumanis, Sonya B.; Richardson, Mark P.; Worrell, Greg; Schulze‐Bonhage, Andreas; Kuhlmann, Levin; Freestone, Dean R.

doi:10.1111/epi.16485

Cited by 98 publications

(88 citation statements)

References 51 publications

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“…35 Importantly, forecasting algorithms based on multiday cycles have emerged as the most accurate, recently surpassing all previous approaches on a benchmark human dataset 6 as well as showing excellent performance for larger cohorts, 36 and using data from seizure diaries. 23 If heart rate can be shown to reliably track multiday cycles of seizure likelihood, then similarly powerful seizure forecasts may eventually be derived from wearable devices.…”

Section: Discussionmentioning

confidence: 99%

“…We had previously demonstrated that multiday cycles established from seizure diaries align with cycles recorded from true electrographic seizures. 23 Nevertheless, it will be critical to validate the co-modulation of heart rate cycles and epileptic activity using chronic EEG recordings. Future work will extend these results to validate the existence of multiday heart rate cycles and their relationship to electrographic seizures using ultra long-term EEG and additional wearable physiological sensors.…”

Section: Discussionmentioning

confidence: 99%

“…These two datasets provided important complementary information. Seizure diaries provide an approximation of underlying cycles of epileptic activity compared to EEG 23 and wearable devices for heart rate monitoring have lower resolution than ECG. However, these limitations are somewhat overcome by the ability to collect long-term recordings over months to years.…”

Section: Methodsmentioning

confidence: 99%

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Multiday cycles of heart rate are associated with seizure likelihood

Karoly

Stirling

Freestone³

et al. 2020

Preprint

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Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological phenomena can provide new insight into the causes of multiday cycles. The brain-heart link is highly relevant for epilepsy, with implications for seizure prediction, therapy (i.e. vagal nerve stimulation) and mortality (i.e. sudden unexpected death in epilepsy).We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study quantified multiday cycles of heart rate and seizures in human participants (N=26) using wearable smartwatches and mobile seizure diaries over at least two months (M=6.4, SD=4.0). Participants had an epilepsy diagnosis and uncontrolled or partially controlled seizures. Significant cycles in heart rate were detected using a continuous wavelet transform. Significant co-modulation between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase (Omnibus test). Weekly trends were also investigated from a retrospective cohort of continuous video-EEG-ECG records of people (N=29) undergoing diagnostic epilepsy monitoring for at least eight days (M=9.7, SD=1.3).Heart rate cycles were found in all participants, with circadian (100%), about-weekly (88%) and about-monthly (92%) rhythms being the most prevalent. Seizures were significantly locked onto at least one heart rate cycle in 76% of eligible participants. Seizures occurred preferentially during the rising phase of the heart rate cycles. Significant weekly trends were also verified in 52% of the video-EEG-ECG cohort.The existence and prevalence of multiday heart rate cycles may have clinical implications for cardiology. Heart rate cycles showed striking similarities to multiday epileptic rhythms and were co-modulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Multiday cycles of heart rate are associated with seizure likelihood

Karoly

Stirling

Freestone³

et al. 2020

Preprint

Self Cite

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“…The probability forecast, though obviously more informative than a binary forecast, will likely be degraded to a binary forecast when algorithms are written to initiate responsive neurostimulation ( 8 ). Even binary forecasting systems (high- and low-risk), using only patient-reported seizure data, correctly predicted seizures in about half of 50 patients ( 125 ).…”

Section: Background For Understanding Seizure Generation Inhibition and Propagationmentioning

confidence: 99%

Prediction of Seizure Recurrence. A Note of Caution

2021

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Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.

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“…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

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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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Forecasting cycles of seizure likelihood

Cited by 98 publications

References 51 publications

Multiday cycles of heart rate are associated with seizure likelihood

Multiday cycles of heart rate are associated with seizure likelihood

Prediction of Seizure Recurrence. A Note of Caution

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

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