Multiday cycles of heart rate are associated with seizure likelihood: An observational cohort study

Karoly, Philippa J.; Stirling, Rachel E; Freestone, Dean R.; Nurse, Ewan S.; Maturana, Matias I.; Halliday, Amy J.; Neal, Andrew; Gregg, Nicholas M.; Brinkmann, Benjamin H.; Richardson, Mark P.; Gerche, André La; Grayden, David B.; D’Souza, Wendyl; Cook, Mark

doi:10.1016/j.ebiom.2021.103619

Cited by 55 publications

(67 citation statements)

References 43 publications

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“…Seizure phase locking to multiscale cycles in HR corroborates recent findings from a long-term study of seizure risk and heart rate cycles using patient reported seizure diaries and a fitness watch. 21 Our work strengthens the finding with electrographic confirmation of seizures from chronic brain recordings, and the use of a multimodal wearable device. Prior work has established that ictal and interictal heart rate changes are common in epilepsy, 22,66 however, only recently have the tools been available to assess for seizure cycles over ultra-long timescales.…”

Section: Discussionsupporting

confidence: 82%

“…False discovery rate correction was performed to account for multiple statistical comparisons. This retrospective analysis of long timescale seizure dynamics, consistent with prior efforts in the field, 3,4,6,12-14,21 used zero-phase shift noncausal analyses, and efforts are underway for the application of cycles based seizure forecasting to prospective trials. The assessment of seizure risk in epilepsy may be improved further by combining long timescale cycles based seizure risk characterization with an acute seizure forecast layer, which has shown early promise with multimodal wearable devices.…”

Section: Discussionsupporting

confidence: 62%

“…This might indicate that periods of increased autonomic arousal (reflected in peak residual HR activity) are associated with times of heightened brain excitability and seizure risk. The proportion of subjects with seizure phase locking to multiday cycles of residual HR is somewhat greater than prior work 21 and may relate to differences in technique (original HR results were similar to prior work).…”

Section: Discussionmentioning

confidence: 59%

“…However, the requirement of chronic brain recordings is a barrier to widespread accessibility. Recent work using data from a wearable fitness device 21 provides exciting evidence that long timescale changes in physiology (heart rate (HR)) may also inform seizure risk cycles. The mechanisms behind the association of seizure risk and multiscale HR cycles in epilepsy are unclear, but may include interactions between brain excitability and autonomic regulation, [21][22][23] in addition to potential behavioral and homeostatic mechanisms.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal wearable sensors inform cycles of seizure risk

Gregg

Attia

Nasseri

et al. 2022

Preprint

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Objective: Seizure unpredictability is a major source of disability for people with epilepsy. Recent work using chronic brain recordings has established that for many individuals with epilepsy seizure risk is not random, but corresponds to circadian and multiday (multidien) cycles in brain excitability. Here, we aimed to evaluate whether multimodal wearable device recordings can characterize cycles of seizure risk, and compare wearables performance with concurrent brain recordings. Methods: Fourteen subjects underwent long-term ambulatory monitoring with a multimodal wrist worn device (measuring heart rate, heart rate variability, accelerometry, tonic and phasic electrodermal activity, temperature) and an implanted responsive neurostimulation system (measuring interictal epileptiform abnormalities (IEA) and electrographic seizures). Wavelet time-frequency analyses identified circadian and multiday cycles in wearable and brain recordings. Circular statistics assessed seizure phase locking to cycles in physiology. Results: Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electrographic seizure detections (mean 76 seizures). Seizure phase locking to multiday cycles occurred in six (IEA), five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Seizure phase locking to residual HR multiday cycles (HR after regression of correlated physical activity (ACC)) increased to six subjects. Interpretation: Long timescale cyclical changes in wearable recordings are common in epilepsy, and seizures occur at preferred phases of these cycles for many individuals, with implications for seizure risk forecasting. Broadly accessible wearable technology can provide new insights into the chronobiology of epilepsy.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionsupporting

confidence: 62%

Section: Discussionmentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

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Multimodal wearable sensors inform cycles of seizure risk

Gregg

Attia

Nasseri

et al. 2022

Preprint

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“…Some patients are nonresponsive to benzodiazepines but instead respond to sodium channel blockers, or there could be responses specific to certain benzodiazepines but not others. Functional magnetic resonance imaging, EEG (e.g., high‐frequency oscillations), heart rate variability, or other biomarkers could provide insights as to the potential of success for some therapies 65,95,96 …”

Section: Looking Into Future Trials What New Paradigms Do You See Eme...mentioning

confidence: 99%

Future opportunities for research in rescue treatments

et al. 2022

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Clinical studies of rescue medications for seizure clusters are limited and are designed to satisfy regulatory requirements, which may not fully consider the needs of the diverse patient population that experiences seizure clusters or utilize rescue medication. The purpose of this narrative review is to examine the factors that contribute to, or may influence the quality of, seizure cluster research with a goal of improving clinical practice. We address five areas of unmet needs and provide advice for how they could enhance future trials of seizure cluster treatments. The topics addressed in this article are: (1) unaddressed end points to pursue in future studies, (2) roles for devices to enhance rescue medication clinical development programs, (3) tools to study seizure cluster prediction and prevention, (4) the value of other designs for seizure cluster studies, and (5) unique challenges of future trial paradigms for seizure clusters. By focusing on novel end points and technologies with value to patients, caregivers, and clinicians, data obtained from future studies can benefit the diverse patient population that experiences seizure clusters, providing more effective, appropriate care as well as alleviating demands on health care resources.

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Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods: A hospital‐based validation study

Jeppesen,

Lin,

Melo

et al. 2024

Epileptic Disorders

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ObjectiveAutomated seizure detection of focal epileptic seizures is needed for objective seizure quantification to optimize the treatment of patients with epilepsy. Heart rate variability (HRV)‐based seizure detection using patient‐adaptive threshold with logistic regression machine learning (LRML) methods has presented promising performance in a study with a Danish patient cohort. The objective of this study was to assess the generalizability of the novel LRML seizure detection algorithm by validating it in a dataset recorded from long‐term video‐EEG monitoring (LTM) in a Brazilian patient cohort.MethodsIctal and inter‐ictal ECG‐data epochs recorded during LTM were analyzed retrospectively. Thirty‐four patients had 107 seizures (79 focal, 28 generalized tonic–clonic [GTC] including focal‐to‐bilateral‐tonic–clonic seizures) eligible for analysis, with a total of 185.5 h recording. Because HRV‐based seizure detection is only suitable in patients with marked ictal autonomic change, patients with >50 beats/min change in heart rate during seizures were selected as responders. The patient‐adaptive LRML seizure detection algorithm was applied to all elected ECG data, and results were computed separately for responders and non‐responders.ResultsThe patient‐adaptive LRML seizure detection algorithm yielded a sensitivity of 84.8% (95% CI: 75.6–93.9) with a false alarm rate of .25/24 h in the responder group (22 patients, 59 seizures). Twenty‐five of the 26 GTC seizures were detected (96.2%), and 25 of the 33 focal seizures without bilateral convulsions were detected (75.8%).SignificanceThe study confirms in a new, independent external dataset the good performance of seizure detection from a previous study and suggests that the method is generalizable. This method seems useful for detecting both generalized and focal epileptic seizures. The algorithm can be embedded in a wearable seizure detection system to alert patients and caregivers of seizures and generate objective seizure counts helping to optimize the treatment of the patients.

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Multiday cycles of heart rate are associated with seizure likelihood: An observational cohort study

Cited by 55 publications

References 43 publications

Multimodal wearable sensors inform cycles of seizure risk

Multimodal wearable sensors inform cycles of seizure risk

Future opportunities for research in rescue treatments

Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods: A hospital‐based validation study

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