Seizure prediction — ready for a new era | Nature Reviews Neurology
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Seizure prediction — ready for a new era

Nature Reviews Neurology volume 14, pages 618–630 (2018)Cite this article

Subjects

Abstract

Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.

Key points

  • One clinical trial has shown that prospective seizure prediction in humans is possible.

  • Databases of EEG data provide a standard reference for comparison of seizure prediction algorithms and for hypothesis generation.

  • Competitions provide a platform for identification of the best seizure prediction algorithms.

  • The network theory of epilepsy, multimodal recording techniques, long-term monitoring and computational modelling are providing new approaches to seizure prediction.

  • The field is ready for a large-scale clinical trial of seizure prediction.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The evolution of seizure prediction.
Fig. 2: Seizure prediction systems.
Fig. 3: Evaluation of seizure prediction algorithms.

Similar content being viewed by others

References

  1. Banerjee, P. N., Filippi, D. & Allen Hauser, W. The descriptive epidemiology of epilepsy-a review. Epilepsy Res. 85, 31–45 (2009).

    PubMed  PubMed Central  Google Scholar 

  2. Kwan, P., Schachter, S. C. & Brodie, M. J. Drug-resistant epilepsy. N. Engl. J. Med. 365, 919–926 (2011).

    CAS  PubMed  Google Scholar 

  3. Ridsdale, L., Charlton, J., Ashworth, M., Richardson, M. P. & Gulliford, M. C. Epilepsy mortality and risk factors for death in epilepsy: a population-based study. Br. J. Gen. Pract. 61, e271–e278 (2011).

    PubMed  PubMed Central  Google Scholar 

  4. Dumanis, S. B., French, J. A., Bernard, C., Worrell, G. A. & Fureman, B. E. Seizure forecasting from idea to reality. Outcomes of the My Seizure Gauge Epilepsy Innovation Institute Workshop. eNeuro 4, ENEURO. 0349–0317.2017 (2017).

    Google Scholar 

  5. Epilepsy-Foundation. Ei2 community survey. Epilepsy-Foundation https://www.epilepsy.com/make-difference/research-and-new-therapies/innovation/epilepsy-innovation-institute/seizure-gauge (2016).

  6. Nickel, R. et al. Quality of life issues and occupational performance of persons with epilepsy. Arq. Neuropsiquiatr. 70, 140–144 (2012).

    PubMed  Google Scholar 

  7. Fisher, R. S. et al. The impact of epilepsy from the patient’s perspective I. Descriptions and subjective perceptions. Epilepsy Res. 41, 39–51 (2000).

    CAS  PubMed  Google Scholar 

  8. Mormann, F., Andrzejak, R. G., Elger, C. E. & Lehnertz, K. Seizure prediction: the long and winding road. Brain 130, 314–333 (2007).

    PubMed  Google Scholar 

  9. Cook, M. J. 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. 12, 563–571 (2013).

    PubMed  Google Scholar 

  10. Kuhlmann, L., Grayden, D. B., Wendling, F. & Schiff, S. J. Role of multiple-scale modeling of epilepsy in seizure forecasting. J. Clin. Neurophysiol. 32, 220–226 (2015).

    PubMed  PubMed Central  Google Scholar 

  11. Snyder, D. E., Echauz, J., Grimes, D. B. & Litt, B. The statistics of a practical seizure warning system. J. Neural Eng. 5, 392–401 (2008).

    PubMed  PubMed Central  Google Scholar 

  12. Winterhalder, M. et al. The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy Behav. 4, 318–325 (2003).

    CAS  PubMed  Google Scholar 

  13. Kuhlmann, L. et al. Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG. Brain https://doi.org/10.1093/brain/awy210 (2018).

  14. Gadhoumi, K., Gotman, J. & Lina, J. M. Scale invariance properties of intracerebral EEG improve seizure prediction in mesial temporal lobe epilepsy. PLOS One 10, e0121182 (2015).

    PubMed  PubMed Central  Google Scholar 

  15. Karoly, P. J. et al. The circadian profile of epilepsy improves seizure forecasting. Brain 140, 2169–2182 (2017).

    PubMed  Google Scholar 

  16. Kiral-Kornek, I. et al. Epileptic seizure prediction using big data and deep learning: toward a mobile system. Ebiomedicine 27, 103–111 (2018).

    PubMed  Google Scholar 

  17. Truong, N. D. et al. Convolutional neural network for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw. 105, 104–111 (2018).

    PubMed  Google Scholar 

  18. Kuhlmann, L. et al. Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons. Epi. Res. 91, 214–231 (2010).

    Google Scholar 

  19. Schelter, B. et al. Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction. Chaos 16, 013108 (2006).

    PubMed  Google Scholar 

  20. Andrzejak, R. G. et al. Testing the null hypothesis of the nonexistence of a preseizure state. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 67, 010901 (2003).

    PubMed  Google Scholar 

  21. Andrzejak, R. G., Chicharro, D., Elger, C. E. & Mormann, F. Seizure prediction: any better than chance? Clin. Neurophysiol. 120, 1465–1478 (2009).

    PubMed  Google Scholar 

  22. Kreuz, T. et al. Measure profile surrogates: a method to validate the performance of epileptic seizure prediction algorithms. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 69, 061915 (2004).

    PubMed  Google Scholar 

  23. Lehnertz, K. & Litt, B. The first international collaborative workshop on seizure prediction: summary and data description. Clin. Neurophysiol. 116, 493–505 (2005).

    PubMed  Google Scholar 

  24. Ihle, M. et al. EPILEPSIAE–a European epilepsy database. Comput. Methods Programs Biomed. 106, 127–138 (2012).

    PubMed  Google Scholar 

  25. Klatt, J. et al. The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients. Epilepsia 53, 1669–1676 (2012).

    PubMed  Google Scholar 

  26. Wagenaar, J. B. et al. Collaborating and sharing data in epilepsy research. J. Clin. Neurophysiol. 32, 235–239 (2015).

    PubMed  PubMed Central  Google Scholar 

  27. Kini, L. G., Davis, K. A. & Wagenaar, J. B. Data integration: combined imaging and electrophysiology data in the cloud. Neuroimage 124, 1175–1181 (2016).

    PubMed  Google Scholar 

  28. Schelter, B. et al. Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies. Epilepsia 47, 2058–2070 (2006).

    PubMed  Google Scholar 

  29. Freestone, D. R., Karoly, P. J. & Cook, M. J. A forward-looking review of seizure prediction. Curr. Opin. Neurol. 30, 167–173 (2017).

    PubMed  Google Scholar 

  30. Cook, M. J. et al. The dynamics of the epileptic brain reveal long-memory processes. Front. Neurol. 5, 217 (2014).

    PubMed  PubMed Central  Google Scholar 

  31. Cook, M. J. et al. Human focal seizures are characterized by populations of fixed duration and interval. Epilepsia 57, 359–368 (2016).

    PubMed  Google Scholar 

  32. Gluckman, B. J. & Schevon, C. A. Seizure prediction 6: from mechanisms to engineered interventions for epilepsy. J. Clin. Neurophysiol. 32, 181–187 (2015).

    PubMed  PubMed Central  Google Scholar 

  33. Kuhlmann, L., Grayden, D. B. & Cook, M. J. Special issue on epilepsy mechanisms, models, prediction and control. Int. J. Neural Syst. 27, 1702001 (2017).

    PubMed  Google Scholar 

  34. Osorio, I., Zaveri, H. P., Frei, M. G. & Arthurs, S. Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics (CRC Press, 2011).

  35. Richardson, M. P. & Jefferys, J. G. Introduction — epilepsy research UK workshop 2010 on “preictal phenomena”. Epilepsy Res. 97, 229–230 (2011).

    PubMed  Google Scholar 

  36. Schelter, B., Timmer, J. & Schulze-Bonhage, A. Seizure Prediction in Epilepsy: from Basic Mechanisms to Clinical Applications (John Wiley & Sons, 2008).

  37. Tetzlaff, R., Elger, C. E. & Lehnertz, K. Recent Advances in Predicting and Preventing Epileptic Seizures (World Scientific, 2013).

  38. Zaveri, H. P., Frei, M. G., Arthurs, S. & Osorio, I. Seizure prediction: the Fourth International Workshop. Epilepsy Behav. 19, 1–3 (2010).

    PubMed  PubMed Central  Google Scholar 

  39. Brinkmann, B. H. et al. Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016).

    PubMed  PubMed Central  Google Scholar 

  40. Howbert, J. J. et al. Forecasting seizures in dogs with naturally occurring epilepsy. PLOS One 9, e81920 (2014).

    PubMed  PubMed Central  Google Scholar 

  41. Karoly, P. J. et al. Interictal spikes and epileptic seizures: their relationship and underlying rhythmicity. Brain 139, 1066–1078 (2016).

    PubMed  Google Scholar 

  42. Payne, D. E. et al. Postictal suppression and seizure durations: a patient-specific, long-term iEEG analysis. Epilepsia 59, 1027–1036 (2018).

    PubMed  Google Scholar 

  43. Lange, H. H., Lieb, J. P., Engel, J. Jr & Crandall, P. H. Temporo-spatial patterns of pre-ictal spike activity in human temporal lobe epilepsy. Electroencephalogr. Clin. Neurophysiol. 56, 543–555 (1983).

    CAS  PubMed  Google Scholar 

  44. Katz, A., Marks, D. A., McCarthy, G. & Spencer, S. S. Does interictal spiking change prior to seizures? Electroencephalogr. Clin. Neurophysiol. 79, 153–156 (1991).

    CAS  PubMed  Google Scholar 

  45. Gotman, J. Relationships between interictal spiking and seizures - human and experimental-evidence. Can. J. Neurol. Sci. 18, 573–576 (1991).

    CAS  PubMed  Google Scholar 

  46. Malow, B. A., Lin, X., Kushwaha, R. & Aldrich, M. S. Interictal spiking increases with sleep depth in temporal lobe epilepsy. Epilepsia 39, 1309–1316 (1998).

    CAS  PubMed  Google Scholar 

  47. Li, S., Zhou, W., Yuan, Q. & Liu, Y. Seizure prediction using spike rate of intracranial EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 21, 880–886 (2013).

    CAS  PubMed  Google Scholar 

  48. Wasade, V. S. et al. Intracranial electrographic analysis of preictal spiking and ictal onset in uni- and bitemporal epilepsy. Epileptic Disord. 17, 156–164 (2015).

    PubMed  Google Scholar 

  49. Goncharova, I. I. et al. The relationship between seizures, interictal spikes and antiepileptic drugs. Clin. Neurophysiol. 127, 3180–3186 (2016).

    PubMed  Google Scholar 

  50. Goncharova, I. I., Zaveri, H. P., Duckrow, R. B., Novotny, E. J. & Spencer, S. S. Spatial distribution of intracranially recorded spikes in medial and lateral temporal epilepsies. Epilepsia 50, 2575–2585 (2009).

    PubMed  Google Scholar 

  51. Goncharova, I. I. et al. Intracranially recorded interictal spikes: relation to seizure onset area and effect of medication and time of day. Clin. Neurophysiol. 124, 2119–2128 (2013).

    PubMed  Google Scholar 

  52. Spencer, S. S., Goncharova, I. I., Duckrow, R. B., Novotny, E. J. & Zaveri, H. P. Interictal spikes on intracranial recording: behavior, physiology, and implications. Epilepsia 49, 1881–1892 (2008).

    PubMed  Google Scholar 

  53. Abou-Khalil, B. The ambiguous relationship between spikes and seizures. Clin. Neurophysiol. 127, 3176–3177 (2016).

    PubMed  Google Scholar 

  54. Baud, M. O. et al. Multi-day rhythms modulate seizure risk in epilepsy. Nat. Commun. 9, 88 (2018).

    Google Scholar 

  55. Gotman, J. A few thoughts on “what is a seizure?”. Epilepsy Behav. 22, S2–S3 (2011).

    PubMed  PubMed Central  Google Scholar 

  56. Staley, K. Molecular mechanisms of epilepsy. Nat. Neurosci. 18, 367–372 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Terry, J. R., Benjamin, O. & Richardson, M. P. Seizure generation: the role of nodes and networks. Epilepsia 53, e166–e169 (2012).

    PubMed  Google Scholar 

  58. Blumenfeld, H. Cellular and network mechanisms of spike-wave seizures. Epilepsia 46 (Suppl. 9), 21–33 (2005).

    CAS  PubMed  Google Scholar 

  59. Schevon, C. A. et al. Cortical abnormalities in epilepsy revealed by local EEG synchrony. Neuroimage 35, 140–148 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Ortega, G. J., Menendez de la Prida, L., Sola, R. G. & Pastor, J. Synchronization clusters of interictal activity in the lateral temporal cortex of epileptic patients: intraoperative electrocorticographic analysis. Epilepsia 49, 269–280 (2008).

    PubMed  Google Scholar 

  61. Gazit, T. et al. Time-frequency characterization of electrocorticographic recordings of epileptic patients using frequency-entropy similarity: a comparison to other bi-variate measures. J. Neurosci. Methods 194, 358–373 (2011).

    CAS  PubMed  Google Scholar 

  62. Palmigiano, A., Pastor, J., Garcia de Sola, R. & Ortega, G. J. Stability of synchronization clusters and seizurability in temporal lobe epilepsy. PLOS One 7, e41799 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Towle, V. L., Carder, R. K., Khorasani, L. & Lindberg, D. Electrocorticographic coherence patterns. J. Clin. Neurophysiol. 16, 528–547 (1999).

    CAS  PubMed  Google Scholar 

  64. Towle, V. L. et al. Identification of the sensory/motor area and pathologic regions using ECoG coherence. Electroencephalogr. Clin. Neurophysiol. 106, 30–39 (1998).

    CAS  PubMed  Google Scholar 

  65. Zaveri, H. P. et al. Localization-related epilepsy exhibits significant connectivity away from the seizure-onset area. Neuroreport 20, 891–895 (2009).

    PubMed  Google Scholar 

  66. Zaveri, H. P., Pincus, S. M., Goncharova, I. I., Duckrow, R. B. & Spencer, S. S. Large scale brain networks in epilepsy. Proc. of SPIE. 70740T https://doi.org/10.1117/12.801365 (2008).

  67. Frei, M. G. et al. Controversies in epilepsy: debates held during the Fourth International Workshop on Seizure Prediction. Epilepsy Behav. 19, 4–16 (2010).

    PubMed  PubMed Central  Google Scholar 

  68. Warren, C. P. et al. Synchrony in normal and focal epileptic brain: the seizure onset zone is functionally disconnected. J. Neurophysiol. 104, 3530–3539 (2010).

    PubMed  PubMed Central  Google Scholar 

  69. Varotto, G., Tassi, L., Franceschetti, S., Spreafico, R. & Panzica, F. Epileptogenic networks of type II focal cortical dysplasia: a stereo-EEG study. Neuroimage 61, 591–598 (2012).

    PubMed  Google Scholar 

  70. Tomlinson, S. B., Porter, B. E. & Marsh, E. D. Interictal network synchrony and local heterogeneity predict epilepsy surgery outcome among pediatric patients. Epilepsia 58, 402–411 (2017).

    PubMed  Google Scholar 

  71. Sinha, N. et al. Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling. Brain 140, 319–332 (2017).

    PubMed  Google Scholar 

  72. Eissa, T. L. & Schevon, C. A. The role of computational modelling in seizure localization. Brain 140, 254–256 (2017).

    PubMed  Google Scholar 

  73. Lee, H. W. et al. Altered functional connectivity in seizure onset zones revealed by fMRI intrinsic connectivity. Neurology 83, 2269–2277 (2014).

    PubMed  PubMed Central  Google Scholar 

  74. Constable, R. T. et al. Potential use and challenges of functional connectivity mapping in intractable epilepsy. Front. Neurol. 4, 39 (2013).

    PubMed  PubMed Central  Google Scholar 

  75. Zhang, X. et al. Social network theory applied to resting-state fMRI connectivity data in the identification of epilepsy networks with iterative feature selection. J. Neurosci. Methods 199, 129–139 (2011).

    PubMed  PubMed Central  Google Scholar 

  76. Negishi, M., Martuzzi, R., Novotny, E. J., Spencer, D. D. & Constable, R. T. Functional MRI connectivity as a predictor of the surgical outcome of epilepsy. Epilepsia 52, 1733–1740 (2011).

    PubMed  PubMed Central  Google Scholar 

  77. Elisevich, K. et al. An assessment of MEG coherence imaging in the study of temporal lobe epilepsy. Epilepsia 52, 1110–1119 (2011).

    PubMed  PubMed Central  Google Scholar 

  78. Nissen, I. A. et al. Identifying the epileptogenic zone in interictal resting-state MEG source-space networks. Epilepsia 58, 137–148 (2017).

    PubMed  Google Scholar 

  79. Englot, D. J. et al. Epileptogenic zone localization using magnetoencephalography predicts seizure freedom in epilepsy surgery. Epilepsia 56, 949–958 (2015).

    PubMed  PubMed Central  Google Scholar 

  80. Englot, D. J. et al. Global and regional functional connectivity maps of neural oscillations in focal epilepsy. Brain 138, 2249–2262 (2015).

    PubMed  PubMed Central  Google Scholar 

  81. Englot, D. J., Konrad, P. E. & Morgan, V. L. Regional and global connectivity disturbances in focal epilepsy, related neurocognitive sequelae, and potential mechanistic underpinnings. Epilepsia 57, 1546–1557 (2016).

    PubMed  PubMed Central  Google Scholar 

  82. Dickten, H., Porz, S., Elger, C. E. & Lehnertz, K. Weighted and directed interactions in evolving large-scale epileptic brain networks. Sci. Rep. 6, 34824 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Lehnertz, K., Dickten, H., Porz, S., Helmstaedter, C. & Elger, C. E. Predictability of uncontrollable multifocal seizures - towards new treatment options. Sci. Rep. 6, 24584 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Sunderam, S., Osorio, I. & Frei, M. G. Epileptic seizures are temporally interdependent under certain conditions. Epilepsy Res. 76, 77–84 (2007).

    PubMed  Google Scholar 

  85. Ouyang, G., Li, X., Dang, C. & Richards, D. A. Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats. Clin. Neurophysiol. 119, 1747–1755 (2008).

    PubMed  Google Scholar 

  86. Ngamga, E. J. et al. Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data. Phys. Lett. A 380, 1419–1425 (2016).

    CAS  Google Scholar 

  87. Staniek, M. & Lehnertz, K. Symbolic transfer entropy. Phys. Rev. Lett. 100, 158101 (2008).

    Google Scholar 

  88. Stamoulis, C., Gruber, L. J., Schomer, D. L. & Chang, B. S. High-frequency neuronal network modulations encoded in scalp EEG precede the onset of focal seizures. Epilepsy Behav. 23, 471–480 (2012).

    PubMed  PubMed Central  Google Scholar 

  89. Dickten, H. & Lehnertz, K. Identifying delayed directional couplings with symbolic transfer entropy. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 90, 062706 (2014).

    PubMed  Google Scholar 

  90. Lehnertz, K. & Dickten, H. Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients. Philos. Trans. A Math. Phys. Eng. Sci. 373, 20140094 (2015).

    PubMed  PubMed Central  Google Scholar 

  91. Reijneveld, J. C., Ponten, S. C., Berendse, H. W. & Stam, C. J. The application of graph theoretical analysis to complex networks in the brain. Clin. Neurophysiol. 118, 2317–2331 (2007).

    PubMed  Google Scholar 

  92. Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009).

    CAS  Google Scholar 

  93. Richardson, M. Current themes in neuroimaging of epilepsy: brain networks, dynamic phenomena, and clinical relevance. Clin. Neurophysiol. 121, 1153–1175 (2010).

    PubMed  Google Scholar 

  94. Kramer, M. A. & Cash, S. S. Epilepsy as a disorder of cortical network organization. Neuroscientist 18, 360–372 (2012).

    PubMed  PubMed Central  Google Scholar 

  95. van Diessen, E., Diederen, S. J. H., Braun, K. P. J., Jansen, F. E. & Stam, C. J. Functional and structural brain networks in epilepsy: what have we learned? Epilepsia 54, 1855–1865 (2013).

    Google Scholar 

  96. Lehnertz, K. et al. Evolving networks in the human epileptic brain. Phys. D Nonlinear Phenomena 267, 7–15 (2014).

    Google Scholar 

  97. Stam, C. J. Modern network science of neurological disorders. Nat. Rev. Neurosci. 15, 683–695 (2014).

    CAS  PubMed  Google Scholar 

  98. Yaffe, R. B. et al. Physiology of functional and effective networks in epilepsy. Clin. Neurophysiol. 126, 227–236 (2015).

    PubMed  Google Scholar 

  99. Bertram, E. H., Zhang, D. X., Mangan, P., Fountain, N. & Rempe, D. Functional anatomy of limbic epilepsy: a proposal for central synchronization of a diffusely hyperexcitable network. Epilepsy Res. 32, 194–205 (1998).

    CAS  PubMed  Google Scholar 

  100. Bragin, A., Wilson, C. L. & Engel, J. Jr. Chronic epileptogenesis requires development of a network of pathologically interconnected neuron clusters: a hypothesis. Epilepsia 41 (Suppl. 6), S144–S152 (2000).

    PubMed  Google Scholar 

  101. Spencer, S. S. Neural networks in human epilepsy: evidence of and implications for treatment. Epilepsia 43, 219–227 (2002).

    PubMed  Google Scholar 

  102. Schindler, K., Leung, H., Elger, C. E. & Lehnertz, K. Assessing seizure dynamics by analysing the correlation structure of multichannel intracranial EEG. Brain 130, 65–77 (2007).

    PubMed  Google Scholar 

  103. Schindler, K., Elger, C. E. & Lehnertz, K. Increasing synchronization may promote seizure termination: evidence from status epilepticus. Clin. Neurophysiol. 118, 1955–1968 (2007).

    PubMed  Google Scholar 

  104. Kramer, M. A., Kolaczyk, E. D. & Kirsch, H. E. Emergent network topology at seizure onset in humans. Epilepsy Res. 79, 173–186 (2008).

    PubMed  Google Scholar 

  105. Schindler, K. A., Bialonski, S., Horstmann, M. T., Elger, C. E. & Lehnertz, K. Evolving functional network properties and synchronizability during human epileptic seizures. Chaos 18, 033119 (2008).

    PubMed  Google Scholar 

  106. Valton, L. et al. Functional interactions in brain networks underlying epileptic seizures in bilateral diffuse periventricular heterotopia. Clin. Neurophysiol. 119, 212–223 (2008).

    PubMed  Google Scholar 

  107. Kramer, M. A. et al. Coalescence and fragmentation of cortical networks during focal seizures. J. Neurosci. 30, 10076–10085 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Bialonski, S., Wendler, M. & Lehnertz, K. Unraveling spurious properties of interaction networks with tailored random networks. PLOS One 6, e22826 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Kramer, M. A. et al. Human seizures self-terminate across spatial scales via a critical transition. Proc. Natl Acad. Sci. USA 109, 21116–21121 (2012).

    CAS  PubMed  Google Scholar 

  110. Stamoulis, C., Schomer, D. L. & Chang, B. S. Information theoretic measures of network coordination in high-frequency scalp EEG reveal dynamic patterns associated with seizure termination. Epilepsy Res. 105, 299–315 (2013).

    PubMed  PubMed Central  Google Scholar 

  111. Liao, W. et al. Dynamical intrinsic functional architecture of the brain during absence seizures. Brain Struct. Funct. 219, 2001–2015 (2014).

    PubMed  Google Scholar 

  112. Afra, P., Jouny, C. C. & Bergey, G. K. Termination patterns of complex partial seizures: an intracranial EEG study. Seizure 32, 9–15 (2015).

    PubMed  PubMed Central  Google Scholar 

  113. Geier, C., Bialonski, S., Elger, C. E. & Lehnertz, K. How important is the seizure onset zone for seizure dynamics? Seizure 25, 160–166 (2015).

    PubMed  Google Scholar 

  114. Steimer, A., Zubler, F. & Schindler, K. Chow-Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series. Neuroimage 118, 520–537 (2015).

    PubMed  Google Scholar 

  115. Gupta, D., Ossenblok, P. & van Luijtelaar, G. Space-time network connectivity and cortical activations preceding spike wave discharges in human absence epilepsy: a MEG study. Med. Biol. Eng. Comput. 49, 555–565 (2011).

    PubMed  Google Scholar 

  116. Takahashi, H., Takahashi, S., Kanzaki, R. & Kawai, K. State-dependent precursors of seizures in correlation-based functional networks of electrocorticograms of patients with temporal lobe epilepsy. Neurol. Sci. 33, 1355–1364 (2012).

    PubMed  Google Scholar 

  117. Clemens, B. et al. Neurophysiology of juvenile myoclonic epilepsy: EEG-based network and graph analysis of the interictal and immediate preictal states. Epilepsy Res. 106, 357–369 (2013).

    CAS  PubMed  Google Scholar 

  118. Geier, C. & Lehnertz, K. Long-term variability of importance of brain regions in evolving epileptic brain networks. Chaos 27, 043112 (2017).

    PubMed  Google Scholar 

  119. Burns, S. P. et al. Network dynamics of the brain and influence of the epileptic seizure onset zone. Proc. Natl Acad. Sci. USA 111, E5321–E5330 (2014).

    CAS  PubMed  Google Scholar 

  120. Kuhnert, M. T., Elger, C. E. & Lehnertz, K. Long-term variability of global statistical properties of epileptic brain networks. Chaos 20, 043126 (2010).

    PubMed  Google Scholar 

  121. Geier, C., Lehnertz, K. & Bialonski, S. Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing. Front. Hum. Neurosci. 9, 462 (2015).

    PubMed  PubMed Central  Google Scholar 

  122. Spencer, D. D., Gerrard, J. L. & Zaveri, H. P. The roles of surgery and technology in understanding focal epilepsy and its comorbidities. Lancet Neurol. 17, 373–382 (2018).

    PubMed  Google Scholar 

  123. Deisseroth, K. & Schnitzer, M. J. Engineering approaches to illuminating brain structure and dynamics. Neuron 80, 568–577 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. Patil, A. C. & Thakor, N. V. Implantable neurotechnologies: a review of micro- and nanoelectrodes for neural recording. Med. Biol. Eng. Comput. 54, 23–44 (2016).

    PubMed  Google Scholar 

  125. Stead, M. et al. Microseizures and the spatiotemporal scales of human partial epilepsy. Brain 133, 2789–2797 (2010).

    PubMed  PubMed Central  Google Scholar 

  126. Truccolo, W. et al. Single-neuron dynamics in human focal epilepsy. Nat. Neurosci. 14, 635–641 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Schevon, C. A. et al. Evidence of an inhibitory restraint of seizure activity in humans. Nat. Commun. 3, 1060 (2012).

    PubMed  PubMed Central  Google Scholar 

  128. Hu, S. Q. et al. Increase trend of correlation and phase synchrony of microwire iEEG before macroseizure onset. Cogn. Neurodyn. 8, 111–126 (2014).

    PubMed  Google Scholar 

  129. Gast, H. et al. Burst firing of single neurons in the human medial temporal lobe changes before epileptic seizures. Clin. Neurophysiol. 127, 3329–3334 (2016).

    PubMed  Google Scholar 

  130. Smith, E. H. et al. The ictal wavefront is the spatiotemporal source of discharges during spontaneous human seizures. Nat. Commun. 7, 11098 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. Petroff, O. A. et al. Glutamate-glutamine cycling in the epileptic human hippocampus. Epilepsia 43, 703–710 (2002).

    CAS  PubMed  Google Scholar 

  132. Cavus, I. et al. Extracellular metabolites in the cortex and hippocampus of epileptic patients. Ann. Neurol. 57, 226–235 (2005).

    CAS  PubMed  Google Scholar 

  133. DiNuzzo, M., Mangia, S., Maraviglia, B. & Giove, F. Physiological bases of the K+ and the glutamate/GABA hypotheses of epilepsy. Epilepsy Res. 108, 995–1012 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Stephens, M. L. et al. Tonic glutamate in CA1 of aging rats correlates with phasic glutamate dysregulation during seizure. Epilepsia 55, 1817–1825 (2014).

    CAS  PubMed  Google Scholar 

  135. Kanamori, K. & Ross, B. D. Chronic electrographic seizure reduces glutamine and elevates glutamate in the extracellular fluid of rat brain. Brain Res. 1371, 180–191 (2011).

    CAS  PubMed  Google Scholar 

  136. Kanamori, K. & Ross, B. D. Electrographic seizures are significantly reduced by in vivo inhibition of neuronal uptake of extracellular glutamine in rat hippocampus. Epilepsy Res. 107, 20–36 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. During, M. J. & Spencer, D. D. Extracellular hippocampal glutamate and spontaneous seizure in the conscious human brain. Lancet 341, 1607–1610 (1993).

    CAS  PubMed  Google Scholar 

  138. Huberfeld, G. et al. Glutamatergic pre-ictal discharges emerge at the transition to seizure in human epilepsy. Nat. Neurosci. 14, 627–U121 (2011).

    CAS  PubMed  Google Scholar 

  139. Lillis, K. P., Kramer, M. A., Mertz, J., Staley, K. J. & White, J. A. Pyramidal cells accumulate chloride at seizure onset. Neurobiol. Dis. 47, 358–366 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Tewolde, S., Oommen, K., Lie, D. Y., Zhang, Y. & Chyu, M. C. Epileptic seizure detection and prediction based on continuous cerebral blood flow monitoring — a review. J. Healthc. Eng. 6, 159–178 (2015).

    PubMed  Google Scholar 

  141. Schwartz, T. H., Hong, S. B., Bagshaw, A. P., Chauvel, P. & Benar, C. G. Preictal changes in cerebral haemodynamics: review of findings and insights from intracerebral EEG. Epilepsy Res. 97, 252–266 (2011).

    PubMed  Google Scholar 

  142. Patel, K. S., Zhao, M., Ma, H. & Schwartz, T. H. Imaging preictal hemodynamic changes in neocortical epilepsy. Neurosurg. Focus 34, E10 (2013).

    PubMed  PubMed Central  Google Scholar 

  143. Nilsen, K. B., Haram, M., Tangedal, S., Sand, T. & Brodtkorb, E. Is elevated pre-ictal heart rate associated with secondary generalization in partial epilepsy? Seizure 19, 291–295 (2010).

    PubMed  Google Scholar 

  144. Delamont, R. S., Julu, P. O. O. & Jamal, G. A. Changes in a measure of cardiac vagal activity before and after epileptic seizures. Epilepsy Res. 35, 87–94 (1999).

    CAS  PubMed  Google Scholar 

  145. Bruno, E., Biondi, A., Richardson, M. P. and RADAR-CNS-Consortium. Pre-ictal heart rate changes: a systematic review and meta-analysis. Seizure 55, 48–56 (2018).

    PubMed  Google Scholar 

  146. Benuzzi, F. et al. Increased cortical BOLD signal anticipates generalized spike and wave discharges in adolescents and adults with idiopathic generalized epilepsies. Epilepsia 53, 622–630 (2012).

    PubMed  Google Scholar 

  147. Masterton, R. A., Carney, P. W., Abbott, D. F. & Jackson, G. D. Absence epilepsy subnetworks revealed by event-related independent components analysis of functional magnetic resonance imaging. Epilepsia 54, 801–808 (2013).

    PubMed  Google Scholar 

  148. Moeller, F. et al. Simultaneous EEG-fMRI in drug-naive children with newly diagnosed absence epilepsy. Epilepsia 49, 1510–1519 (2008).

    PubMed  Google Scholar 

  149. Bai, X. et al. Dynamic time course of typical childhood absence seizures: EEG, behavior, and functional magnetic resonance imaging. J. Neurosci. 30, 5884–5893 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Carney, P. W. et al. The core network in absence epilepsy. Differences in cortical and thalamic BOLD response. Neurology 75, 904–911 (2010).

    CAS  PubMed  Google Scholar 

  151. Moeller, F. et al. Changes in activity of striato-thalamo-cortical network precede generalized spike wave discharges. Neuroimage 39, 1839–1849 (2008).

    PubMed  Google Scholar 

  152. Zhao, M. et al. Focal increases in perfusion and decreases in hemoglobin oxygenation precede seizure onset in spontaneous human epilepsy. Epilepsia 48, 2059–2067 (2007).

    PubMed  Google Scholar 

  153. Baumgartner, C. et al. Preictal SPECT in temporal lobe epilepsy: regional cerebral blood flow is increased prior to electroencephalography-seizure onset. J. Nucl. Med. 39, 978–982 (1998).

    CAS  PubMed  Google Scholar 

  154. Bauer, P. R., Kalitzin, S., Zijlmans, M., Sander, J. W. & Visser, G. H. Cortical excitability as a potential clinical marker of epilepsy: a review of the clinical application of transcranial magnetic stimulation. Int. J. Neural Syst. 24, 1430001 (2014).

    PubMed  Google Scholar 

  155. Wright, M. A., Orth, M., Patsalos, P. N., Smith, S. J. & Richardson, M. P. Cortical excitability predicts seizures in acutely drug-reduced temporal lobe epilepsy patients. Neurology 67, 1646–1651 (2006).

    PubMed  Google Scholar 

  156. Badawy, R., Macdonell, R., Jackson, G. & Berkovic, S. The peri-ictal state: cortical excitability changes within 24 h of a seizure. Brain 132, 1013–1021 (2009).

    PubMed  Google Scholar 

  157. Suffczynski, P. et al. Active paradigms of seizure anticipation: computer model evidence for necessity of stimulation. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 78, 051917 (2008).

    PubMed  Google Scholar 

  158. O’Sullivan-Greene, E., Mareels, I., Freestone, D., Kulhmann, L. & Burkitt, A. in Annual International Conference of the IEEE Engineering in Medicine and Biology Society 6428–6431 (2009).

  159. Kalitzin, S. N., Velis, D. N. & da Silva, F. H. Stimulation-based anticipation and control of state transitions in the epileptic brain. Epilepsy Behav. 17, 310–323 (2010).

    PubMed  Google Scholar 

  160. Kalitzin, S., Koppert, M., Petkov, G., Velis, D. & da Silva, F. L. Computational model prospective on the observation of proictal states in epileptic neuronal systems. Epilepsy Behav. 22 (Suppl. 1), S102–S109 (2011).

    PubMed  Google Scholar 

  161. Bruzzo, A. A., Gesierich, B., Rubboli, G. & Vimal, R. L. Predicting epileptic seizures with a mental simulation task: a prospective study. Epilepsy Behav. 13, 256–259 (2008).

    PubMed  Google Scholar 

  162. Freestone, D. R. et al. Electrical probing of cortical excitability in patients with epilepsy. Epilepsy Behav. 22 (Suppl. 1), S110–S118 (2011).

    PubMed  Google Scholar 

  163. Meisel, C. et al. Intrinsic excitability measures track antiepileptic drug action and uncover increasing/decreasing excitability over the wake/sleep cycle. Proc. Natl Acad. Sci. USA 112, 14694–14699 (2015).

    CAS  PubMed  Google Scholar 

  164. Lytton, W. W. Computer modelling of epilepsy. Nat. Rev. Neurosci. 9, 626–637 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  165. Soltesz, I. & Staley, K. Computational Neuroscience in Epilepsy Vol. 6 (Academic Press, 2008).

  166. Stefanescu, R. A., Shivakeshavan, R. G. & Talathi, S. S. Computational models of epilepsy. Seizure 21, 748–759 (2012).

    PubMed  Google Scholar 

  167. Volman, V., Bazhenov, M. & Sejnowski, T. J. Computational models of neuron-astrocyte interaction in epilepsy. Front. Comput. Neurosci. 6, 58 (2012).

    PubMed  PubMed Central  Google Scholar 

  168. Holt, A. B. & Netoff, T. I. Computational modeling of epilepsy for an experimental neurologist. Exp. Neurol. 244, 75–86 (2013).

    PubMed  Google Scholar 

  169. Wendling, F., Benquet, P., Bartolomei, F. & Jirsa, V. Computational models of epileptiform activity. J. Neurosci. Methods 260, 233–251 (2016).

    PubMed  Google Scholar 

  170. Chakravarthy, N., Sabesan, S., Tsakalis, K. & Iasemidis, L. Controlling epileptic seizures in a neural mass model. J. Combinatorial Optimiz. 17, 98–116 (2009).

    Google Scholar 

  171. Shayegh, F., Fattahi, R. A., Sadri, S. & Ansari-Asl, K. A. Brief survey of computational models of normal and epileptic EEG Signals: a guideline to model-based seizure prediction. J. Med. Signals Sens. 1, 62–72 (2011).

    PubMed  PubMed Central  Google Scholar 

  172. Shayegh, F., Sadri, S., Amirfattahi, R. & Ansari-Asl, K. Proposing a two-level stochastic model for epileptic seizure genesis. J. Comput. Neurosci. 36, 39–53 (2014).

    CAS  PubMed  Google Scholar 

  173. Freestone, D. et al. in Recent advances in predicting and preventing epileptic seizures (eds Tetzlaff, R., Elger, C. E. & Lehnertz, K.) 63–82 (World Scientific, 2013).

  174. Freestone, D. R. et al. Seizure prediction: science fiction or soon to become reality? Curr. Neurol. Neurosci. Rep. 15, 73 (2015).

    PubMed  Google Scholar 

  175. Aram, P., Freestone, D. R., Cook, M. J., Kadirkamanathan, V. & Grayden, D. B. Model-based estimation of intra-cortical connectivity using electrophysiological data. Neuroimage 118, 563–575 (2015).

    CAS  PubMed  Google Scholar 

  176. Watson, P. D., Horecka, K. M., Cohen, N. J. & Ratnam, R. A. Phase-locked loop epilepsy network emulator. Neurocomputing 173, 1245–1249 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  177. Milton, J. G. Epilepsy as a dynamic disease: a tutorial of the past with an eye to the future. Epilepsy Behav. 18, 33–44 (2010).

    PubMed  Google Scholar 

  178. Lopes da Silva, F., Blanes, W., Parra, S. N. K. J., Suffczynski, P. & Velis, D. N. Epilepsies as dynamical diseases of brain systems: basic models of the transition between normal and epileptic activity. Epilepsia 44 (Suppl. 12), 72–83 (2003).

    PubMed  Google Scholar 

  179. Rabinovich, M. I., Varona, P., Selverston, A. I. & Abarbanel, H. D. I. Dynamical principles in neuroscience. Rev. Mod. Phys. 78, 1213–1265 (2006).

    Google Scholar 

  180. Meisel, C., Storch, A., Hallmeyer-Elgner, S., Bullmore, E. & Gross, T. Failure of adaptive self-organized criticality during epileptic seizure attacks. PLOS Comput. Biol. 8, e1002312 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  181. Milton, J. G. Neuronal avalanches, epileptic quakes and other transient forms of neurodynamics. Eur. J. Neurosci. 36, 2156–2163 (2012).

    PubMed  Google Scholar 

  182. Suffczynski, P., Kalitzin, S. & Lopes Da Silva, F. H. Dynamics of non-convulsive epileptic phenomena modeled by a bistable neuronal network. Neuroscience 126, 467–484 (2004).

    CAS  PubMed  Google Scholar 

  183. Takeshita, D., Sato, Y. D. & Bahar, S. Transitions between multistable states as a model of epileptic seizure dynamics. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 75, 051925 (2007).

    PubMed  Google Scholar 

  184. Jirsa, V. K., Stacey, W. C., Quilichini, P. P., Ivanov, A. I. & Bernard, C. On the nature of seizure dynamics. Brain 137, 2210–2230 (2014).

    PubMed  PubMed Central  Google Scholar 

  185. Feldt, S., Osterhage, H., Mormann, F., Lehnertz, K. & Zochowski, M. Internetwork and intranetwork communications during bursting dynamics: applications to seizure prediction. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 76, 021920 (2007).

    CAS  PubMed  Google Scholar 

  186. Rothkegel, A. & Lehnertz, K. Multistability, local pattern formation, and global collective firing in a small-world network of non-leaky integrate-and-fire neurons. Chaos 19, 015109 (2009).

    PubMed  Google Scholar 

  187. Rothkegel, A. & Lehnertz, K. Recurrent events of synchrony in complex networks of pulse-coupled oscillators. Europhys. Lett. 95, 38001 (2011).

    Google Scholar 

  188. Anderson, W. S., Azhar, F., Kudela, P., Bergey, G. K. & Franaszczuk, P. J. Epileptic seizures from abnormal networks: why some seizures defy predictability. Epilepsy Res. 99, 202–213 (2012).

    PubMed  Google Scholar 

  189. Baier, G., Goodfellow, M., Taylor, P. N., Wang, Y. J. & Garry, D. J. The importance of modeling epileptic seizure dynamics as spatio-temporal patterns. Front. Physiol. 3, 281 (2012).

    PubMed  PubMed Central  Google Scholar 

  190. Ansmann, G., Karnatak, R., Lehnertz, K. & Feudel, U. Extreme events in excitable systems and mechanisms of their generation. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 88, 052911 (2013).

    PubMed  Google Scholar 

  191. Petkov, G., Goodfellow, M., Richardson, M. P. & Terry, J. R. A critical role for network structure in seizure onset: a computational modeling approach. Front. Neurol. 5, 261 (2014).

    PubMed  PubMed Central  Google Scholar 

  192. Ansmann, G., Lehnertz, K. & Feudel, U. Self-induced pattern switching on complex networks of excitable units. Phys. Rev. X 6, 011030 (2016).

    Google Scholar 

  193. Schiff, S. J. Neural Control Engineering: the Emerging Intersection Between Control Theory and Neuroscience (MIT Press, 2012).

  194. Chong, M. S., Nešic´, D., Postoyan, R. & Kuhlmann, L. Parameter and state estimation of nonlinear systems using a multi-observer under the supervisory framework. IEEE Trans. Automat. Control 60, 2336–2349 (2015).

    Google Scholar 

  195. Kuhlmann, L. et al. Neural mass model-based tracking of anesthetic brain states. Neuroimage 133, 438–456 (2016).

    PubMed  Google Scholar 

  196. Xian, L., Qing, G. & Xiao-Li, L. Control of epileptiform spikes based on nonlinear unscented Kalman filter. Chinese Phys. B 23, 010202 (2013).

    Google Scholar 

  197. Taylor, P. N. et al. Optimal control based seizure abatement using patient derived connectivity. Front. Neurosci. 9, 202 (2015).

    PubMed  PubMed Central  Google Scholar 

  198. Wei, Y., Ullah, G. & Schiff, S. J. Unification of neuronal spikes, seizures, and spreading depression. J. Neurosci. 34, 11733–11743 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  199. De Ciantis, A. & Lemieux, L. Localisation of epileptic foci using novel imaging modalities. Curr. Opin. Neurol. 26, 368–373 (2013).

    PubMed  PubMed Central  Google Scholar 

  200. Krook-Magnuson, E., Armstrong, C., Oijala, M. & Soltesz, I. On-demand optogenetic control of spontaneous seizures in temporal lobe epilepsy. Nat. Commun. 4, 1376 (2013).

    PubMed  PubMed Central  Google Scholar 

  201. Badawy, R. A., Freestone, D. R., Lai, A. & Cook, M. J. Epilepsy: ever-changing states of cortical excitability. Neuroscience 222, 89–99 (2012).

    CAS  PubMed  Google Scholar 

  202. Bazaka, K. & Jacob, M. V. Implantable devices: issues and challenges. Electronics 2, 1–34 (2012).

    Google Scholar 

  203. Jory, C. et al. Safe and sound? A systematic literature review of seizure detection methods for personal use. Seizure 36, 4–15 (2016).

    PubMed  Google Scholar 

  204. Patel, A. D. et al. Patient-centered design criteria for wearable seizure detection devices. Epilepsy Behav. 64, 116–121 (2016).

    PubMed  Google Scholar 

  205. Ramgopal, S. et al. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav. 37, 291–307 (2014).

    PubMed  Google Scholar 

  206. Van de Vel, A. et al. Non-EEG seizure detection systems and potential SUDEP prevention: state of the art: review and update. Seizure 41, 141–153 (2016).

    PubMed  Google Scholar 

  207. Arthurs, S., Zaveri, H. P., Frei, M. G. & Osorio, I. Patient and caregiver perspectives on seizure prediction. Epilepsy Behav. 19, 474–477 (2010).

    PubMed  Google Scholar 

  208. Schulze-Bonhage, A. et al. Views of patients with epilepsy on seizure prediction devices. Epilepsy Behav. 18, 388–396 (2010).

    PubMed  Google Scholar 

  209. Hoppe, C. et al. Novel techniques for automated seizure registration: patients’ wants and needs. Epilepsy Behav. 52, 1–7 (2015).

    PubMed  Google Scholar 

  210. Hoppe, C., Poepel, A. & Elger, C. E. Epilepsy: accuracy of patient seizure counts. Arch. Neurol. 64, 1595–1599 (2007).

    PubMed  Google Scholar 

  211. Blachut, B. et al. Counting seizures: The primary outcome measure in epileptology from the patients’ perspective. Seizure 29, 97–103 (2015).

    PubMed  Google Scholar 

  212. Elger, C. E. & Hoppe, C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol. 17, 279–288 (2018).

    PubMed  Google Scholar 

  213. Johansson, D., Malmgren, K. & Murphy, M. A. Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: a mixed-methods systematic review. J. Neurol. 265, 1740–1752 (2018).

    PubMed  PubMed Central  Google Scholar 

  214. Vandecasteele, K. et al. Automated epileptic seizure detection based on wearable ECG and PPG in a hospital environment. Sensors (Basel) 17, 2338 (2017).

    Google Scholar 

  215. Gadhoumi, K., Lina, J. M., Mormann, F. & Gotman, J. Seizure prediction for therapeutic devices: a review. J. Neurosci. Methods 260, 270–282 (2016).

    PubMed  Google Scholar 

  216. Fisher, R. et al. Electrical stimulation of the anterior nucleus of thalamus for treatment of refractory epilepsy. Epilepsia 51, 899–908 (2010).

    PubMed  Google Scholar 

  217. Sun, F. T. & Morrell, M. J. The RNS System: responsive cortical stimulation for the treatment of refractory partial epilepsy. Expert Rev. Med. Devices 11, 563–572 (2014).

    CAS  PubMed  Google Scholar 

  218. Yuan, H. & Silberstein, S. D. Vagus nerve and vagus nerve stimulation, a comprehensive review: part II. Headache 56, 259–266 (2016).

    PubMed  Google Scholar 

  219. Duun-Henriksen, J. et al. Subdural to subgaleal EEG signal transmission: the role of distance, leakage and insulating affectors. Clin. Neurophysiol. 124, 1570–1577 (2013).

    PubMed  Google Scholar 

  220. Stacey, W. C. & Litt, B. Technology insight: neuroengineering and epilepsy — designing devices for seizure control. Nat. Clin. Pract. Neurol. 4, 190–201 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  221. Brinkmann, B. H. et al. Forecasting seizures using intracranial EEG measures and SVM in naturally occurring canine epilepsy. PLOS One 10, e0133900 (2015).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors acknowledge colleagues in the international seizure prediction group for valuable discussions. L.K. acknowledges funding support from the National Health and Medical Research Council (APP1130468) and the James S. McDonnell Foundation (220020419) and acknowledges the contribution of Dean R. Freestone at the University of Melbourne, Australia, to the creation of Fig. 3.

Review criteria

We searched PubMed, Web of Science, Google Scholar and IEEExplore with the terms “seizure prediction”, “seizure anticipation”, “seizure forecasting” and “preictal” for human and animal studies published between 1 January 2006 and 30 June 2018. We did not restrict publications by language. We also manually searched the proceedings of seizure prediction workshops and the reference lists of papers identified and extracted relevant papers from our records.

Author information

Authors and Affiliations

  1. Centre for Human Psychopharmacology, Swinburne University of Technology, Melbourne, Victoria, Australia

    Levin Kuhlmann

  2. Department of Medicine – St. Vincent’s, The University of Melbourne, Parkville, Victoria, Australia

    Levin Kuhlmann

  3. Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia

    Levin Kuhlmann

  4. Department of Epileptology, University of Bonn, Bonn, Germany

    Klaus Lehnertz

  5. Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany

    Klaus Lehnertz

  6. Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Mark P. Richardson

  7. Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK

    Björn Schelter

  8. Department of Neurology, Yale University, New Haven, CT, USA

    Hitten P. Zaveri

Authors
  1. Levin Kuhlmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Klaus Lehnertz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mark P. Richardson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Björn Schelter
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hitten P. Zaveri
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed equally to the conception of the Review, reviewing of the literature, writing and editing. L.K. designed the figures and tables with feedback from K.L., M.R., B.S. and H.Z.

Corresponding author

Correspondence to Klaus Lehnertz.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

American Epilepsy Society Seizure Prediction Challenge: www.kaggle.com/c/seizure-prediction

EPILEPSIAE: www.epilepsiae.eu

Epilepsy Ecosystem: www.epilepsyecosystem.org

IEEG.org: www.ieeg.org

Melbourne University AES/MathWorks/NIH Seizure Prediction: https://www.kaggle.com/c/melbourne-university-seizure-prediction

RADAR-CNS: https://www.radar-cns.org

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuhlmann, L., Lehnertz, K., Richardson, M.P. et al. Seizure prediction — ready for a new era. Nat Rev Neurol 14, 618–630 (2018). https://doi.org/10.1038/s41582-018-0055-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41582-018-0055-2

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research