Literature DB >> 32219856

Forecasting cycles of seizure likelihood.

Philippa J Karoly1,2, Mark J Cook1, Matias Maturana1,3, Ewan S Nurse1,3, Daniel Payne2, Benjamin H Brinkmann4, David B Grayden2, Sonya B Dumanis5, Mark P Richardson6, Greg A Worrell4, Andreas Schulze-Bonhage7,8, Levin Kuhlmann9, Dean R Freestone3.   

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. Wiley Periodicals, Inc.
© 2020 International League Against Epilepsy.

Entities:  

Keywords:  circadian rhythms; epilepsy; mobile health; multiday rhythms; seizure cycles; seizure forecasting

Year:  2020        PMID: 32219856     DOI: 10.1111/epi.16485

Source DB:  PubMed          Journal:  Epilepsia        ISSN: 0013-9580            Impact factor:   5.864


  16 in total

1.  Forecasting seizure risk in adults with focal epilepsy: a development and validation study.

Authors:  Timothée Proix; Wilson Truccolo; Marc G Leguia; Thomas K Tcheng; David King-Stephens; Vikram R Rao; Maxime O Baud
Journal:  Lancet Neurol       Date:  2020-12-17       Impact factor: 44.182

2.  Seizure forecasting using minimally invasive, ultra-long-term subcutaneous electroencephalography: Individualized intrapatient models.

Authors:  Pedro F Viana; Tal Pal Attia; Mona Nasseri; Jonas Duun-Henriksen; Andrea Biondi; Joel S Winston; Isabel Pavão Martins; Ewan S Nurse; Matthias Dümpelmann; Andreas Schulze-Bonhage; Dean R Freestone; Troels W Kjaer; Mark P Richardson; Benjamin H Brinkmann
Journal:  Epilepsia       Date:  2022-04-08       Impact factor: 6.740

3.  Prediction of Seizure Recurrence. A Note of Caution.

Authors:  William J Bosl; Alan Leviton; Tobias Loddenkemper
Journal:  Front Neurol       Date:  2021-05-13       Impact factor: 4.003

4.  Epileptic Seizure Cycles: Six Common Clinical Misconceptions.

Authors:  Philippa J Karoly; Dean R Freestone; Dominique Eden; Rachel E Stirling; Lyra Li; Pedro F Vianna; Matias I Maturana; Wendyl J D'Souza; Mark J Cook; Mark P Richardson; Benjamin H Brinkmann; Ewan S Nurse
Journal:  Front Neurol       Date:  2021-08-04       Impact factor: 4.003

5.  Evaluation and recommendations for effective data visualization for seizure forecasting algorithms.

Authors:  Sharon Chiang; Robert Moss; Angela P Black; Michele Jackson; Chuck Moss; Jonathan Bidwell; Christian Meisel; Tobias Loddenkemper
Journal:  JAMIA Open       Date:  2021-03-01

6.  Remote and Long-Term Self-Monitoring of Electroencephalographic and Noninvasive Measurable Variables at Home in Patients With Epilepsy (EEG@HOME): Protocol for an Observational Study.

Authors:  Andrea Biondi; Petroula Laiou; Elisa Bruno; Pedro F Viana; Martijn Schreuder; William Hart; Ewan Nurse; Deb K Pal; Mark P Richardson
Journal:  JMIR Res Protoc       Date:  2021-03-19

7.  Evidence of state-dependence in the effectiveness of responsive neurostimulation for seizure modulation.

Authors:  Sharon Chiang; Ankit N Khambhati; Emily T Wang; Marina Vannucci; Edward F Chang; Vikram R Rao
Journal:  Brain Stimul       Date:  2021-02-06       Impact factor: 8.955

Review 8.  Cycles in epilepsy.

Authors:  Philippa J Karoly; Vikram R Rao; Maxime O Baud; Nicholas M Gregg; Gregory A Worrell; Christophe Bernard; Mark J Cook
Journal:  Nat Rev Neurol       Date:  2021-03-15       Impact factor: 42.937

Review 9.  Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic.

Authors:  Benjamin H Brinkmann; Philippa J Karoly; Ewan S Nurse; Sonya B Dumanis; Mona Nasseri; Pedro F Viana; Andreas Schulze-Bonhage; Dean R Freestone; Greg Worrell; Mark P Richardson; Mark J Cook
Journal:  Front Neurol       Date:  2021-07-13       Impact factor: 4.003

10.  Evidence for long memory in focal seizure duration.

Authors:  Joline M Fan; Sharon Chiang; Vikram R Rao
Journal:  Epilepsia Open       Date:  2021-01-07
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