OBJECTIVE: Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient-specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts. METHODS: This study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0-2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location-specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective. RESULTS: For the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature. SIGNIFICANCE: Environmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.

Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Prague Czech Republic

Department of Biomedical Engineering The University of Melbourne Melbourne Victoria Australia

Department of Data Science and AI Faculty of IT Monash University Clayton Victoria Australia

Department of Medicine St Vincent's Hospital The University of Melbourne Melbourne Victoria Australia

Department of Neurology Mayo Clinic Rochester MN USA

Graeme Clark Institute The University of Melbourne Melbourne Victoria Australia

See more in PubMed

Epilepsy Foundation. Ei2 community survey. 2016. https://www.epilepsy.com/sites/core/files/atoms/files/community-survey-report-2016%20V2.pdf. Accessed 14 Dec 2018.

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;2:1–5. PubMed PMC

Fisher RS, Vickrey BG, Gibson P, Hermann B, Penovich P, Scherer A, et al. The impact of epilepsy from the patient’s perspective I. Descriptions and subjective perceptions. Epilepsy Res. 2000;41:39–51. PubMed

Cook MJ, Brien TJO, Berkovic SF, Murphy M, Morokoff A, Fabinyi G, 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. PubMed

Kuhlmann L, Karoly P, Freestone DR, Brinkmann BH, Temko A, Barachant A, et al. Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG. Brain. 2018;141:2619–30. PubMed PMC

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. PubMed

Freestone DR, Karoly PJ, Cook MJ. A forward-looking review of seizure prediction. Curr Opin Neurol. 2017;30(2):1. PubMed

Bedeeuzzaman M, Fathima T, Khan YU, Farooq O. Seizure prediction using statistical dispersion measures of intracranial EEG. Biomed Signal Process Control. 2013;10:338–41.

Costa RP, Oliveira P, Rodrigues G, Leitão B, Dourado A. Epileptic seizure classification using neural networks with 14 features. In: Lovrek I, Howlett RJ, Jain LC, editors. Knowledge-based intelligent information and engineering systems: 12th international conference, KES 2008, Zagreb, Croatia, September 3–5, 2008, proceedings, part II. Berlin, Heidelberg, Germany: Springer; 2008. p. 281–8.

Winterhalder M, Schelter B, Maiwald T, Brandt A, Schad A, Schulze-Bonhage A, et al. Spatio-temporal patient-individual assessment of synchronization changes for epileptic seizure prediction. Clin Neurophysiol. 2006;117(11):2399–413. PubMed

Mormann F, Andrzejak RG, Elger CE, Lehnertz K. Seizure prediction: the long and winding road. Brain. 2007;130:314–33. PubMed

Park Y, Luo L, Parhi KK, Netoff T. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia. 2011;52(10):1761–70. PubMed

Le Van Quyen M, Soss J, Navarro V, Robertson R, Chavez M, Baulac M, et al. Preictal state identification by synchronization changes in long-term intracranial EEG recordings. Clin Neurophysiol. 2005;116(3):559–68. PubMed

Ghaderyan P, Abbasi A, Sedaaghi MH. An efficient seizure prediction method using KNN-based undersampling and linear frequency measures. J Neurosci Methods. 2014;232:134–42. PubMed

Schelter B, Feldwisch-Drentrup H, Ihle M, Schulze-Bonhage A, Timmer J. Seizure prediction in epilepsy: from circadian concepts via probabilistic forecasting to statistical evaluation. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1624–7. PubMed

Parvez MZ, Paul M. Seizure prediction using undulated global and local features. IEEE Trans Biomed Eng. 2017;64(1):208–17. PubMed

Kuhlmann L, Freestone D, Lai A, Burkitt AN, Fuller K, Grayden DB, et al. Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons. Epilepsy Res. 2010;91(2–3):214–31. PubMed

Mirowski P, Madhavan D, LeCun Y, Kuzniecky R. Classification of patterns of EEG synchronization for seizure prediction. Clin Neurophysiol. 2009;120(11):1927–40. PubMed

Kiral-Kornek I, Roy S, Nurse E, Mashford B, Karoly P, Carroll T, et al. Epileptic seizure prediction using big data and deep learning: toward a mobile system. EBioMedicine. 2017;27:103–11. PubMed PMC

Spalt J, Langbauer G, Boltzmann L. Subjective perception of seizure precipitants: results of a questionnaire study. Seizure. 1998;7:391–5. PubMed

Bercel NA. The periodic features of some seizure states. Ann N Y Acad Sci. 1964;117:555–63. PubMed

Karoly PJ, Ung H, Grayden DB, Kuhlmann L, Leyde K, Cook MJ, et al. The circadian profile of epilepsy improves seizure forecasting. Brain. 2017;140(8):2169–82. PubMed

Baud MO, Kleen JK, Mirro EA, Andrechak JC, King-Stephens D, Chang EF, et al. Multi-day rhythms modulate seizure risk in epilepsy. Nat Commun. 2018;9:1–10. PubMed PMC

Rakers F, Walther M, Schiffner R, Rupprecht S, Rasche M, Kockler M, et al. Weather as a risk factor for epileptic seizures: a case-crossover study. Epilepsia. 2017;58(7):1287–95. PubMed

Ellingson RJ, Wilken K, Bennett DR. Efficacy of sleep deprivation as an activation procedure in epilepsy patients. J Clin Neurophysiol. 1984;1(1):83–101. PubMed

Foldvary-Schaefer N, Grigg-Damberger M. Sleep and epilepsy: what we know, don’t know, and need to know. J Clin Neurophysiol. 2006;23(1):4–20. PubMed

Rossi KC, Joe J, Makhija M, Goldenholz DM. Insufficient sleep, electroencephalogram activation, and seizure risk: re-evaluating the evidence. Ann Neurol. 2020;87(6):798–806. PubMed

Russel SJ, Norvig P. Bayes rule and its use. In: Hirsch M, editor. Artificial intelligence: a modern approach. 3rd ed. Hoboken, NJ: Pearson; 2010. p. 495–9.

Freestone DR, Karoly PJ, Peterson ADH, Kuhlmann L, Lai A, Goodarzy F, et al. Seizure prediction: science fiction or soon to become reality? Curr Neurol Neurosci Rep. 2015;15:73. PubMed

Baud MO, Proix T, Rao VR, Schindler K. Chance and risk in epilepsy. Curr Opin Neurol. 2020;33(2):163–72. PubMed

Litt B, Lehnertz K. Seizure prediction and the preseizure period. Curr Opin Neurol. 2002;15(2):173–7. PubMed

Ung H, Baldassano SN, Bink H, Krieger AM, Williams S, Vitale F, et al. Fluctuates over months after implanting electrodes in human brain. J Neural Eng. 2017;14(5):056011. PubMed PMC

Nejedly P, Kremen V, Sladky V, Cimbalnik J, Klimes P, Plesinger F, et al.. Exploiting graphoelements and convolutional neural networks with long short term memory for classification of the human electroencephalogram. Sci Rep. 2019;9(1):2–10. PubMed PMC

Kremen V, Brinkmann BH, Van Gompel JJ, Stead M, St Louis EK, Worrell GA, et al. Automated unsupervised behavioral state classification using intracranial electrophysiology. J Neural Eng. 2019;16(2):1–8. PubMed

Gerla V, Kremen V, Macas M, Dudysova D, Mladek A, Sos P, et al. Iterative expert-in-the-loop classification of sleep PSG recordings using a hierarchical clustering. J Neurosci Methods. 2019;317:61–70. PubMed

Cajochen C, Altanay-Ekici S, Münch M, Frey S, Knoblauch V, Wirz-Justice A. Evidence that the lunar cycle influences human sleep. Curr Biol. 2013;23:1485–8. PubMed

Schulze-Bonhage A, Sales F, Wagner K, Teotonio R, Carius A, Schelle A, et al. Views of patients with epilepsy on seizure prediction devices. Epilepsy Behav. 2010;18(4):388–96. PubMed

Arthurs S, Zaveri HP, Frei MG, Osorio I. Patient and care-giver perspectives on seizure prediction. Epilepsy Behav. 2010;19(3):474–7. PubMed

Domingos P, Pazzani M. Beyond independence: conditions for the optimality of the simple Bayesian classifier. In: Saitta L, editor. Machine learning 1996 international conference: proceedings of the thirteenth international conference. San Francisco, CA: Morgan Kaufmann; 1996. p. 1–8.

Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148(3):839–43. PubMed

Cobabe MM, Sessler DI, Nowacki AS, O’Rourke C, Andrews N, Foldvary-Schaefer N. Impact of sleep duration on seizure frequency in adults with epilepsy: a sleep diary study. Epilepsy Behav. 2015;43:143–8. PubMed

Samsonsen C, Sand T, Bråthen G, Helde G, Brodtkorb E. The impact of sleep loss on the facilitation of seizures: a prospective case-crossover study. Epilepsy Res. 2016;127:260–6. PubMed

Frauscher B, von Ellenrieder N, Ferrari-Marinho T, Avoli M, Dubeau F, Gotman J, et al. Facilitation of epileptic activity during sleep is mediated by high amplitude slow waves. Brain. 2015;138:1629–41. PubMed PMC

Doherty MJ, Youn C, Gwinn RP, Haltiner AM. Atmospheric pressure and seizure frequency in the epilepsy unit: preliminary observations. Epilepsia. 2007;48(9):1764–7. PubMed

Quigg M, Straume M. Dual epileptic foci in a single patient express distinct temporal patterns dependent on limbic versus nonlimbic brain location. Ann Neurol. 2001;48(1):117–20. PubMed

Karoly PJ, Goldenholz DM, Freestone DR, Moss RE, Grayden DB, Theodore WH, et al. Circadian and circaseptan rhythms in human epilepsy: a retrospective cohort study. Lancet Neurol. 2018;17(11):977–85. PubMed

Maturana MI, Meisel C, Dell K, Karoly PJ, D’Souza W, Grayden DB, et al. Critical slowing as a biomarker for seizure susceptibility. Nat Commun. 2020;11:2172. PubMed PMC

Cook MJ, Karoly PJ, Freestone DR, Himes D, Leyde K, Berkovic S, et al. Human focal seizures are characterized by populations of fixed duration and interval. Epilepsia. 2016;57(3):359–68. PubMed

Payne DE, Karoly PJ, Freestone DR, Boston R, D’Souza W, Nurse E, et al. Postictal suppression and seizure durations: a patient-specific, long-term iEEG analysis. Epilepsia. 2018;59(5): 1027–36. PubMed

Kerem DH, Geva AB. Forecasting epilepsy from the heart rate signal. Med Biol Eng Comput. 2005;43:230–9. PubMed

Fujiwara K, Miyajima M, Yamakawa T, Abe E, Suzuki Y, Sawada Y, et al.. Epileptic seizure prediction based on multivariate statistical process control of heart rate variability features. IEEE Trans Biomed Eng. 2015;63(6):1321–32. PubMed