Multiday cycles of heart rate are associated with seizure likelihood

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:10.1101/2020.11.24.20237990

Cited by 8 publications

(13 citation statements)

References 57 publications

(115 reference statements)

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“…This is concordant with previous work with intracranial devices (NeuroVista and RNS Systems) 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 (40), cycles were detected at circadian and multiday periodicities for one individual (Figure 3B), with 18-day and 29-day cycles in EA showing the strongest relationship with seizure timing (Figure 3C,D). Interestingly, multiday cycles of EA in this subject were stronger than the circadian rhythm.…”

Section: Discussionsupporting

confidence: 91%

“…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%

“…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), 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 patientspecific.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Stirling

Karoly

Maturana³

et al. 2021

Preprint

Self Cite

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“…Seizures are known to follow circadian and multiday cycles in most people, so there was a specific focus on finding physiological cycles with similar periodicities using wearable signals. This builds on our previous work that found a relationship between cycles in heart rate and seizure occurrence (30). The study was approved by the St Vincent's Hospital Human Research Ethics Committee (HREC 009.…”

Section: Study Design and Participantsmentioning

confidence: 92%

“…Periodic wearable signals, such as temperature (40) and heart rate (30) may also be used as a biomarker for seizure cycles (35). For example, our recent work in seizure timing and heart rate, measured from a wearable smartwatch, shows that seizures are often phase-locked to underlying circadian and multiday cycles in heart rate (i.e., there is a strong preference for seizures to occur at specific phases of the heart rate cycle) (30).…”

Section: Introductionmentioning

confidence: 99%

Forecasting seizure likelihood with wearable technology

Stirling

Grayden

D’Souza

et al. 2021

Preprint

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The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated with seizures but need to be feasible and accessible in the long-term. Wearable devices are perfect candidates to develop non-invasive, accessible forecasts but are yet to be investigated in long-term studies. We hypothesized that machine learning models could utilize heart rate as a biomarker for well-established cycles of seizures and epileptic activity, in addition to other wearable signals, to forecast high and low risk seizure periods. This feasibility study tracked participants' (n = 11) heart rates, sleep, and step counts using wearable smartwatches and seizure occurrence using mobile seizure diaries for at least 6 months (mean = 14.6 months, SD = 3.8 months). Eligible participants had a diagnosis of refractory epilepsy and reported at least 20 seizures (mean = 135, SD = 123) during the recording period. An ensembled machine learning and neural network model estimated seizure risk either daily or hourly, with retraining occurring on a weekly basis as additional data was collected. Performance was evaluated retrospectively against a rate-matched random forecast using the area under the receiver operating curve. A pseudo-prospective evaluation was also conducted on a held-out dataset. Of the 11 participants, seizures were predicted above chance in all (100%) participants using an hourly forecast and in ten (91%) participants using a daily forecast. The average time spent in high risk (prediction time) before a seizure occurred was 37 minutes in the hourly forecast and 3 days in the daily forecast. Cyclic features added the most predictive value to the forecasts, particularly circadian and multiday heart rate cycles. Wearable devices can be used to produce patient-specific seizure forecasts, particularly when biomarkers of seizure and epileptic activity cycles are utilized.

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