Forecasting cycles of seizure likelihood.
circadian rhythms
epilepsy
mobile health
multiday rhythms
seizure cycles
seizure forecasting
Journal
Epilepsia
ISSN: 1528-1167
Titre abrégé: Epilepsia
Pays: United States
ID NLM: 2983306R
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
received:
15
10
2019
revised:
01
03
2020
accepted:
02
03
2020
pubmed:
29
3
2020
medline:
21
10
2020
entrez:
29
3
2020
Statut:
ppublish
Résumé
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. 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. 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. 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.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
776-786Subventions
Organisme : National Health and Medical Research Council
ID : APP 1130468
Pays : International
Organisme : National Health and Medical Research Council
ID : APP 1178220
Pays : International
Organisme : Epilepsy Foundation
Pays : International
Informations de copyright
Wiley Periodicals, Inc. © 2020 International League Against Epilepsy.
Références
Dumanis SB, French JA, Bernard C, Worrell GA, Fureman BE. Seizure forecasting from idea to reality. Outcomes of the My Seizure Gauge epilepsy innovation institute workshop. eNeuro. 2017;4(6).
Kwan P, Brodie MJ. Early identification of refractory epilepsy. N Engl J Med. 2000;342(5):314-9.
Chen Z, Brodie MJ, Liew D, Kwan P. Treatment outcomes in patients with newly diagnosed epilepsy treated with established and new antiepileptic drugs: a 30-year longitudinal cohort study. JAMA Neurol. 2018;75(3):279-86.
Haut SR, Vouyiouklis M, Shinnar S. Stress and epilepsy: a patient perception survey. Epilepsy Behav. 2003;4(5):511-4.
Shouse MN, da Silva AM, Sammaritano M. Circadian rhythm, sleep, and epilepsy. J Clin Neurophysiol. 1996;13(1):32-50.
Nakken KO. Clinical research physical exercise in outpatients with epilepsy. Epilepsia. 1999;40(5):643-51.
Allen CN. Circadian rhythms, diet and neuronal excitability. Epilepsia. 2008;49(Suppl 8):124-6.
Chang K-C, Wu T-H, Fann JC-Y, et al. Low ambient temperature as the only meteorological risk factor of seizure occurrence: a multivariate study. Epilepsy Behav. 2019;100:106283.
Frucht MM, Quigg M, Schwaner C, Fountain NB. Distribution of seizure precipitants among epilepsy syndromes. Epilepsia. 2000;41(12):1534-9.
Cramer JA, Glassman M, Rienzi V. The relationship between poor medication compliance and seizures. Epilepsy Behav. 2002;3(4):338-42.
Cook MJ, O’Brien TJ, Berkovic SF, et al. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol. 2013;12(6):563-71.
Freestone DR, Karoly PJ, Cook MJ. A forward-looking review of seizure prediction. Current Opin Neurol. 2017;30(2):167-73.
Kuhlmann L, Karoly P, Freestone DR, et al. Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG. Brain. 2018;141:2619-30.
Karoly PJ, Ung H, Grayden DB, et al. The circadian profile of epilepsy improves seizure forecasting. Brain. 2017;140(8):2169-82.
Karoly PJ, Goldenholz DM, Freestone DR, et al. Circadian and circaseptan rhythms in human epilepsy: a retrospective cohort study. Lancet Neurol. 2018;17(11):977-85.
Baud MO, Kleen JK, Mirro EA, et al. Multi-day rhythms modulate seizure risk in epilepsy. Nat Commun. 2018;9(1):88.
Baud MO, Rao VR. Gauging seizure risk. Neurology. 2018;91(21):967-73.
Maturana MI, Meisel C, Dell K, et al. Critical slowing as a biomarker for seizure susceptibility. Nat Commun. 2020; forthcoming.
Proix T, Truccolo W, Leguia MG, King-Stephens D, Rao VR, Baud MO. Forecasting seizure risk over days. medRxiv. 2019:19008086.
Kuhlmann L, Lehnertz K, Richardson MP, Schelter B, Zaveri HP. Seizure prediction-ready for a new era. Nat Rev Neurol. 2018;14(10):618-30.
Schulze-Bonhage A, Sales F, Wagner K, et al. Views of patients with epilepsy on seizure prediction devices. Epilepsy Behav. 2010;18(4):388-96.
Bruno E, Simblett S, Lang A, et al. Wearable technology in epilepsy: the views of patients, caregivers, and healthcare professionals. Epilepsy Behav. 2018;85:141-9.
Janse SA, Dumanis SB, Huwig T, Hyman S, Fureman BE, Bridges JFP. Patient and caregiver preferences for the potential benefits and risks of a seizure forecasting device: a best-worst scaling. Epilepsy Behav. 2019;96:183-91.
Ramgopal S, Thome-Souza S, Jackson M, et al. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav. 2014;37:291-307.
Ulate-Campos A, Coughlin F, Gaínza-Lein M, Fernández IS, Pearl Pl, Loddenkemper T. Automated seizure detection systems and their effectiveness for each type of seizure. Seizure. 2016;40:88-101.
Onorati F, Regalia G, Caborni C, et al. Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors. Epilepsia. 2017;58(11):1870-9.
Haut SR, Hall CB, Borkowski T, Tennen H, Lipton RB. Clinical features of the pre-ictal state: mood changes and premonitory symptoms. Epilepsy Behav. 2012;23(4):415-21.
Haut SR, Hall CB, Borkowski T, Tennen H, Lipton RB. Modeling seizure self-prediction: an e-diary study. Epilepsia. 2013;54(11):1960-7.
Billeci L, Marino D, Insana L, Vatti G, Varanini M. Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis. PloS One. 2018;13(9):e0204339.
Bruno E, Biondi A, Richardson MP. Pre-ictal heart rate changes: a systematic review and meta-analysis. Seizure. 2018;55:48-56.
Meisel C, Atrache RE, Jackson M, Schubach S, Ufongene C, Loddenkemper T. Deep learning from wristband sensor data: towards wearable, non-invasive seizure forecasting. arXiv. 2019:1906.00511.
Berens P. CircStat: a MATLAB toolbox for circular statistics. J Stat Softw. 2009;31(10):1-21.
Satopää VA, Baron J, Foster DP, Mellers BA, Tetlock PE, Ungar LH. Combining multiple probability predictions using a simple logit model. Int J Forecast. 2014;30(2):344-56.
Snyder DE, Echauz J, Grimes DB, Litt B. The statistics of a practical seizure warning system. J Neural Eng. 2008;5(4):392.
Brinkmann BH, Wagenaar J, Abbot D, et al. Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain. 2016;139(6):1713-22.
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29-36.
Hoppe C, Poepel A, Elger CE. Epilepsy: accuracy of patient seizure counts. Arch Neurol. 2007;64(11):1595-9.
Kiral-Kornek I, Roy S, Nurse E, et al. Epileptic seizure prediction using big data and deep learning: toward a mobile system. EBioMedicine. 2018;27:103-11.
Karoly PJ, Goldenholz DM, Cook MJ. Are the days of counting seizures numbered? Curr Opin Neurol. 2018;31(2):162-8.
Haut SR, Gursky JM, Privitera M. Behavioral interventions in epilepsy. Curr Opin Neurol. 2019;32(2):227-36.
Loddenkemper T. Chrono-epileptology: time to reconsider seizure timing. Seizure. 2012;21(6):411.
Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol. 2018;17(3):279-88.
Foster RG, Roenneberg T. Human responses to the geophysical daily, annual and lunar cycles. Curr Biol. 2008;18(17):R784-94.
Bass J. Circadian topology of metabolism. Nature. 2012;491(7424):348-56.
Reinberg AE, Dejardin L, Smolensky MH, Touitou Y. Seven-day human biological rhythms: an expedition in search of their origin, synchronization, functional advantage, adaptive value and clinical relevance. Chronobiol Int. 2017;34(2):162-91.
Nicolau GY, Haus E, Popescu M, Sackett-Lundeen L, Petrescu E. Orcadian, weekly, and seasonal variations in cardiac mortality, blood pressure, and catecholamine excretion. Chronobiol Int. 1991;8(2):149-59.
Gallerani M, Pala M, Fedeli U. Circaseptan periodicity of cardiovascular diseases. Heart Failure Clin. 2017;13(4):703-17.
Haus E, Smolensky MH. Biologic rhythms in the immune system. Chronobiol Int. 1999;16(5):581-622.
Farez MF, Mascanfroni ID, Méndez-Huergo SP, et al. Melatonin contributes to the seasonality of multiple sclerosis relapses. Cell. 2015;162(6):1338-52.
Takaesu Y. Circadian rhythm in bipolar disorder: a review of the literature. Psychiatry Clin Neurosci. 2018;72(9):673-82.
Luoni C, Bisulli F, Canevini MP, et al. Determinants of health-related quality of life in pharmacoresistant epilepsy: results from a large multicenter study of consecutively enrolled patients using validated quantitative assessments. Epilepsia. 2011;52(12):2181-91.