Abstract found on Wiley Online Library
Objective: Epilepsy monitoring unit (EMU) admissions are critical for presurgical evaluation of drug-resistant epilepsy but may be non-diagnostic if an insufficient number of seizures are recorded. Seizure forecasting algorithms have shown promise for estimating the likelihood of seizures as a binary event in individual patients, but methods to predict how many seizures will occur remain elusive. Such methods could increase the diagnostic yield of EMU admissions and help patients mitigate seizure-related morbidity. Here, we evaluated the performance of a state-space method that uses prior seizure count data to predict future counts.
Methods: A Bayesian negative-binomial dynamic linear model (DLM) a type of statistical analysis] [was developed to forecast daily electrographic seizure counts in 19 patients implanted with the responsive neurostimulation (RNS) device. Hold-out validation was used to evaluate performance in predicting the number of electrographic seizures for forecast horizons ranging from 1-7?days ahead.
Results: One-day-ahead prediction of the number of electrographic seizures using a negative-binomial DLM resulted in improvement over chance in 73.1% of time segments compared to a random chance forecaster and remained greater than 50% for forecast horizons of up to 7?days. Superior performance (mean error 0.99) was obtained in predicting the number of electrographic seizures in the next day compared to three traditional methods for count forecasting (INGARCH 1.10, Croston 1.06, GLARMA 2.00). Number of electrographic seizures in the preceding day and laterality of electrographic pattern detections had highest predictive value, with greater number of electrographic seizures and RNS magnet swipes in the preceding day associated with a higher number of electrographic seizures the next day.
Significance: This study demonstrates that dynamic linear model [a type of statistical analysis] can predict the number of electrographic seizures a patient will experience days in advance with above chance accuracy. This study represents an important step toward the translation of seizure forecasting methods into the optimization of epilepsy monitoring unit admissions.