Contents lists available at ScienceDirect Epilepsy Research journal homepage: www.elsevier.com/locate/epilepsyres Individualizing the definition of seizure clusters based on temporal clustering analysis Sharon Chiang a,b, *, Sheryl R. Haut c , Victor Ferastraoaru c , Vikram R. Rao a , Maxime O. Baud d , William H. Theodore e , Robert Moss b,f , Daniel M. Goldenholz g a Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States b EpilepsyAI, LLC, San Francisco, CA, United States c Department of Neurology, Montefiore Medical Center/Albert Einstein College of Medicine, New York, NY, United States d Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland e Clinical Epilepsy Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, United States f Seizure Tracker, LLC, Springfield, VA, United States g Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States ARTICLE INFO Keywords: Seizure clustering Electronic seizure diaries Hurst statistics Change-point analysis ABSTRACT Objective: Seizure clusters are often encountered in people with poorly controlled epilepsy. Detection of seizure clusters is currently based on simple clinical rules, such as two seizures separated by four or fewer hours or multiple seizures in 24 h. Current definitions fail to distinguish between statistically significant clusters and those that may result from natural variation in the person’s seizures. Ability to systematically define when a seizure cluster is significant for the individual carries major implications for treatment. However, there is no uniform consensus on how to define seizure clusters. This study proposes a principled statistical approach to defining seizure clusters that addresses these issues. Methods: A total of 533,968 clinical seizures from 1,748 people with epilepsy in the Seizure Tracker™ seizure diary database were used for algorithm development. We propose an algorithm for automated individualized seizure cluster identification combining cumulative sum change-point analysis with bootstrapping and aberra- tion detection, which provides a new approach to personalized seizure cluster identification at user-specified levels of clinical significance. We develop a standalone user interface to make the proposed algorithm accessible for real-time seizure cluster identification (ClusterCalc™). Clinical impact of systematizing cluster identification is demonstrated by comparing empirically-defined clusters to those identified by routine seizure cluster definitions. We also demonstrate use of the Hurst exponent as a standardized measure of seizure clustering for comparison of seizure clustering burden within or across patients. Results: Seizure clustering was present in 26.7 % (95 % CI, 24.5–28.7 %) of people with epilepsy. Empirical tables were provided for standardizing inter- and intra-patient comparisons of seizure cluster tendency. Using the proposed algorithm, we found that 37.7–59.4 % of seizures identified as clusters based on routine definitions had high probability of occurring by chance. Several clusters identified by the algorithm were missed by con- ventional definitions. The utility of the ClusterCalc algorithm for individualized seizure cluster detection is de- monstrated. Significance: This study proposes a principled statistical approach to individualized seizure cluster identification and demonstrates potential for real-time clinical usage through ClusterCalc. Using this approach accounts for individual variations in baseline seizure frequency and evaluates statistical significance. This new definition has the potential to improve individualized epilepsy treatment by systematizing identification of unrecognized seizure clusters and preventing unnecessary intervention for random events previously considered clusters. 1. Introduction It has long been recognized that some people with epilepsy experience seizures that occur with short inter-seizure intervals, often referred to as seizure “clusters.” Seizure clusters increase morbidity and mortality and negatively impact quality of life (Haut et al., 1999; https://doi.org/10.1016/j.eplepsyres.2020.106330 Received 9 July 2019; Received in revised form 29 March 2020; Accepted 31 March 2020 ⁎ Corresponding author at: PO Box 225039, San Francisco, CA, 94122, United States. E-mail address: Sharon.Chiang@ucsf.edu (S. Chiang). Epilepsy Research 163 (2020) 106330 Available online 09 April 2020 0920-1211/ © 2020 Elsevier B.V. All rights reserved. T