Forecasting cycles of seizure likelihood. (27th March 2020)

• Record Type: Journal Article
• Authors: Karoly, Philippa J.; Cook, Mark J.; Maturana, Matias; Nurse, Ewan S.; Payne, Daniel; Brinkmann, Benjamin H.; Grayden, David B.; Dumanis, Sonya B.; Richardson, Mark P.; Worrell, Greg A.; Schulze‐Bonhage, Andreas; Kuhlmann, Levin; Freestone, Dean R.
• Is Part Of: Epilepsia. Volume 61:issue 4(2020)
• Page Start: 776
• Page End: 786
• Publication Date: 2020-03-27
• Subjects: circadian rhythms -- epilepsy -- mobile health -- multiday rhythms -- seizure cycles -- seizure forecasting; Epilepsy -- Periodicals; 616.853
• DOI: 10.1111/epi.16485
• Languages: English
• ISSNs: 0013-9580

Abstract

Abstract: Objective: Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times, and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self‐reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app. Methods: Forecasts based on circadian and multiday seizure cycles were tested pseudoprospectively using data from 50 app users (mean of 109 seizures per subject). Individuals' strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self‐reported events and electrographic seizures from the Neurovista dataset, an existing database of long‐term electroencephalography that has been widely used to develop forecasting algorithms. Results: The validation dataset showed that forecasts of seizure likelihood based on self‐reported cycles were predictive of electrographic seizures for approximately half the cohort. Forecasts using only mobile app diaries allowed users to spend an average of 67.1% of their time in a low‐risk state, with 14.8% of their time in a high‐risk warning state. On average, 69.1% of seizures occurred during high‐risk states and 10.5% of seizures occurred in low‐risk states. Significance: Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.