Individualizing the definition of seizure clusters based on temporal clustering analysis

Chiang, Sharon; Haut, Sheryl R.; Ferastraoaru, Victor; Rao, Vikram R.; Baud, Maxime O.; Theodore, William H.; Moss, Robert; Goldenholz, Daniel M.

doi:10.1016/j.eplepsyres.2020.106330

Cited by 24 publications

(27 citation statements)

References 27 publications

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“…Seizure clusters were defined using unsupervised temporal clustering via change‐point analysis with bootstrapping. This approach allows for unsupervised seizure cluster detection while enabling for adjustments to satisfy clinical criteria 25 . The change‐point threshold is a manually adjusted parameter that accounts for differences in seizure frequency affecting the definition of meaningful seizure clusters.…”

Section: Methodsmentioning

confidence: 99%

“…This approach allows for unsupervised seizure cluster detection while enabling for adjustments to satisfy clinical criteria. 25 The change-point threshold is a manually adjusted parameter that accounts for differences in seizure frequency affecting the definition of meaningful seizure clusters. The changepoint threshold was titrated for each subject based on two criteria: (a) The maximum intracluster ISI was between 6 and 24 hours, and (b) the mean intracluster ISI was closest to 1.5 hours without exceeding 1.5 hours, depending on the direction of titration (ie, if the mean intracluster ISI was below 1.5 hours, the value was increased to be the maximal value less than 1.5 hours, and vice versa if above 1.5 hours).…”

Section: Data Processingmentioning

confidence: 99%

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Evidence for long memory in focal seizure duration

2021

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Objective A major source of disability for people with epilepsy involves uncertainty surrounding seizure timing and severity. Although patients often report that long seizure‐free intervals are followed by more severe seizures, there is little experimental evidence supporting this observation. Optimal characterization of seizure severity is debated; however, seizure duration is associated with seizure type and can be quantified in electrographic recordings as a limited proxy of clinical seizure severity. Here, using chronic intracranial electroencephalography (cEEG), we investigate the relationship between interseizure interval (ISI) and duration of the subsequent seizure. Methods We performed a retrospective analysis of 14 subjects implanted with a responsive neurostimulation device (RNS System) that provides cEEG, including timestamps of electrographic seizures. We determined seizure durations for isolated seizures and for representative seizures from clusters determined through unsupervised methods. For each subject, the median ISI preceding long‐duration seizures, defined as the top quintile of seizure durations, was compared with the median ISI preceding seizures with durations in the residual quintiles. In a group analysis, the mean seizure duration and the proportion of long‐duration seizures were compared across ISI categories representing different lengths. Results For 5 out of 14 subjects (36%), the median ISI preceding long‐duration seizures was significantly greater than the median ISI preceding shorter‐duration seizures. In the group analysis, when ISI was categorized by length, the proportion of long‐duration seizures within the high ISI category was significantly higher than that of the low ISI category (P < 0.001). Significance By leveraging cEEG and accounting for seizure clusters, we found that the likelihood of long‐duration seizures positively correlates with ISI length, in a subset of individuals. These findings corroborate anecdotal clinical observations and support the existence of capacitor‐like long memory processes governing the dynamics of focal seizures.

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Section: Methodsmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

Evidence for long memory in focal seizure duration

2021

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“…4,5 No consensus definition has been agreed on for this term in the literature or among health care providers (HCPs), patients, and caregivers. 6,7 In the clinical environment, this may lead to a gap in communication between the HCP and the patient and caregiver regarding how seizure clusters should be identified. 8 In addition, the use of a variety of terms to label these potential seizure emergencies may lead to confusion for patients and caregivers.…”

Section: Introductionmentioning

confidence: 99%

“…Although status epilepticus has an accepted definition put forth by the International League Against Epilepsy (ILAE), this is not the case for seizure clusters 4,5 . No consensus definition has been agreed on for this term in the literature or among health care providers (HCPs), patients, and caregivers 6,7 . In the clinical environment, this may lead to a gap in communication between the HCP and the patient and caregiver regarding how seizure clusters should be identified 8 .…”

Section: Introductionmentioning

confidence: 99%

Recognizing seizure clusters in the community: The path to uniformity and individualization in nomenclature and definition

Haut

Nabbout

2022

Epilepsia

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Seizure emergencies experienced by patients with epilepsy include status epilepticus and seizure clusters. Although an accepted definition of status epilepticus exists, no clear consensus definition of seizure clusters has emerged; this is further complicated by the appearance in the literature of various empirically based definitions that have been developed for clinical trial study designs. In general, patients with intractable epilepsy have been shown to have a significant risk for acute episodes of increased seizure activity called seizure clusters (also referred to as acute repetitive seizures, among other terms) that differ from their usual seizure pattern. Duration (e.g., number of hours or days) is often included in the definition of a seizure cluster; however, the duration may vary among patients, with some seizure clusters lasting ≥24 h and requiring long‐acting treatment for this period. In addition to seizure cluster duration, the time between seizures and possible acceleration in seizure frequency during the cluster may be important variables. The recognition and treatment of seizure clusters require urgent action because episodes that are not quickly and appropriately treated may lead to injury or progress to status epilepticus or potentially death. Most seizure clusters occur outside a medical facility (in the community) and treatment is usually administered by nonmedical individuals; therefore, health care providers may benefit from a clear description of these potential seizure emergencies that they can then use to educate patients and caregivers on the prompt and appropriate identification of seizure clusters and administration of rescue therapy. Here we explore why greater uniformity is needed in the discussion of seizure clusters. This exploration examines epidemiologic studies of seizure clusters and status epilepticus, inconsistencies in nomenclature and definitions for seizure clusters, practical application of seizure cluster terminology, and the potential use of acute seizure action plans and patient‐specific individualized definitions in the clinical setting.

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“…Given the replication crisis across many fields including psychology and the neurosciences, there is an increased emphasis on displaying all data points in a clear explicit manner (see Allen et al, 2019 for data visualisation using raincloud plots). As well as being more transparent, examination of individual performances and data may lead to a better evaluation of outliers, the comparison of performance in various settings (e.g in education, see Braithwaite et al, 2019), the examination of individual diagnosis or treatment plans (Chiang et al, 2020;Simon, 2001) and the ability to account for individual behavioural patterns (Seidler et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Examining individual learning patterns using generalized linear mixed models

Commins¹

2022

Preprint

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Everyone learns differently, but individual performances are often ignored in favour of a group level analysis. Using data from four different experiments we show that generalized linear mixed models (GLMMs) and extensions can be used to examine individual learning patterns in more depth. Producing ellipsoids and cluster analyses based on predicted random effects, individual learning patterns can be identified which, in turn, can be clustered and used for comparisons across various experimental conditions or groups. We show that this analysis can handle a range of data sets including discrete, continuous, censored and non-censored, as well as different experimental conditions, different sample sizes and trial numbers. Using this approach we show that learning a face-named paired associative task produced individuals that can learn quickly with the performance of some remaining high, but with a drop off in others. Whereas other individuals show poor performance throughout the learning period. We see this more clearly when we examined learning in a virtual navigation spatial learning task (NavWell). Two prominent clusters of learning emerged, one showing individuals that produced a rapid learning performance and a second group that learned the task slowly and gradually. We can also identify how individual learning patterns change across trials. Using data from another spatial learning task (SeaHero Quest), we show that performance of individuals mirror their age category. However, not all individuals perform according to their age, some show a pattern that more readily fits a younger category, while others show patterns that fit better with an older age cohort. Overall, using this analytical approach may help practitioners in education and medicine to identify those that might need extra help and attention. In addition, identifying learning patterns may allow further investigation of the underlying neural, biological, environmental and other factors of these individuals.

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Individualizing the definition of seizure clusters based on temporal clustering analysis

Cited by 24 publications

References 27 publications

Evidence for long memory in focal seizure duration

Evidence for long memory in focal seizure duration

Recognizing seizure clusters in the community: The path to uniformity and individualization in nomenclature and definition

Examining individual learning patterns using generalized linear mixed models

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