Forecasting seizure clusters from chronic ambulatory electrocorticography

Seizure clusters are seizures that occur in rapid succession during periods of heightened seizure risk and are associated with substantial morbidity and sudden unexpected death in epilepsy. The objective of this feasibility study was to evaluate the performance of a novel seizure cluster forecasting...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Epilepsia (Copenhagen) Ročník 63; číslo 9; s. e106 - e111
Hlavní autoři: Ilyas, Adeel, Hoffman, Clarissa, Vakilna, Yash, Chaliyeduth, Sreekanth, Muhlhofer, Wolfgang, Riley, Kristen O., Dabaghian, Yuri, Lhatoo, Samden D., Pati, Sandipan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Wiley Subscription Services, Inc 01.09.2022
Témata:
Algorithms
ambulatory electrocorticography
Convulsions & seizures
Epilepsy
Feasibility studies
forecasting
Morbidity
seizure clusters
Seizures
Statistical analysis
ISSN:0013-9580, 1528-1167, 1528-1167
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Seizure clusters are seizures that occur in rapid succession during periods of heightened seizure risk and are associated with substantial morbidity and sudden unexpected death in epilepsy. The objective of this feasibility study was to evaluate the performance of a novel seizure cluster forecasting algorithm. Chronic ambulatory electrocorticography recorded over an average of 38 months in 10 subjects with drug‐resistant epilepsies was analyzed pseudoprospectively by dividing data into training (first 85%) and validation periods. For each subject, the probability of seizure clustering, derived from the Kolmogorov–Smirnov statistic using a novel algorithm, was forecasted in the validation period using individualized autoregressive models that were optimized from training data. The primary outcome of this study was the mean absolute scaled error (MASE) of 1‐day horizon forecasts. From 10 subjects, 394 ± 142 (mean ± SD) electrocorticography‐based seizure events were extracted for analysis, representing a span of 38 ± 27 months of recording. MASE across all subjects was .74 ± .09, .78 ± .09, and .83 ± .07 at .5‐, 1‐, and 2‐day horizons. The feasibility study demonstrates that seizure clusters are quasiperiodic and can be forecasted to clinically meaningful horizons. Pending validation in larger cohorts, the forecasting approach described herein may herald chronotherapy during imminent heightened seizure vulnerability.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0013-9580
1528-1167
1528-1167
DOI:10.1111/epi.17347