Academia.eduAcademia.edu

Outline

Title
Abstract
Key Takeaways
Figures
[H1] Introduction
[H2] Databases and Associated Studies
[H2] Improved Databases and Contests
[H2] Expansion of Continuous Data Collection
[H2] Defining Outcomes Relevant to Patients
[H1] Conclusions
Box 2 | Future Technologies, Devices and Industry
Related Links
Review Criteria
References

Seizure prediction - ready for a new era

Profile image of Levin KuhlmannLevin Kuhlmann

2018, Nature reviews. Neurology

https://doi.org/10.1038/S41582-018-0055-2
visibility

…

description

34 pages

link

1 file

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, se...

Key takeaways
sparkles

AI

  1. Prospective seizure prediction in humans is now demonstrated as feasible and effective in clinical trials.
  2. Over 30% of epilepsy patients remain medically intractable, highlighting the need for prediction methods.
  3. Recent databases and competitions have advanced algorithm development and standardization in seizure prediction research.
  4. The network theory of epilepsy and multi-modal techniques are reshaping our understanding of seizure mechanisms.
  5. Continued collaboration is necessary for translating seizure prediction from research to practical, clinical applications.
Figures (5)
Supplementary table 2 | Performance information for human seizure prediction studies from 2006 to 2018.
Supplementary table 2 | Performance information for human seizure prediction studies from 2006 to 2018.

Related papers

Epileptic Seizure Prediction: An overview
International Journal Of engineering and Computer Science (IJECS)

Epilepsy is a neurological disorder marked by sudden recurrent episodes of sensory disturbance, loss of consciousness, or convulsions, associated with abnormal electrical activity in the brain. The sudden and seemingly unpredictable nature of seizures is one of the most compromising aspects of the disease epilepsy. Most epilepsy patients only spend a marginal part of their time actually having a seizure and show no clinical signs of their disease during the time between seizures, the so-called inter-ictal interval. But the constant fear of the next seizure and the feeling of helplessness associated with it often have a strong impact on the everyday life of a patient (Fisher et al. 2000). A method capable of reliably predicting the occurrence of seizures could significantly improve the quality of life for these patients and open new therapeutic possibilities. Apart from simple warning devices, fully automated closed-loop seizure prevention systems are conceivable. Treatment concepts could move from preventive strategies towards on demand therapy which resets brain dynamics and minimize the risk during epilepsy.

View PDFchevron_right
Epileptic Seizure Prediction
Manoranjan Paul

—In this study a seizure prediction method is proposed based on a patient-specific approach by extracting undulated global and local features of preictal/ ictal and interictal periods of EEG signals. The proposed method consists of feature extraction, classification, and regularization. The undulated global feature is extracted using phase correlation between two consecutive epochs of EEG signals and an undulated local feature is extracted using the fluctuation and deviation of EEG signals within the epoch. These features are further used for classification of preictal/ictal and interictal EEG signals. A regularization technique is applied on the classified outputs for the reduction of false alarms and improvement of the overall prediction accuracy (PA). The experimental results confirm that the proposed method provides high PA (i.e. 95.4%) with low false positive per hour using intracranial EEG signals in different brain locations of 21 patients from a benchmark data set. Combining global and local features enables the transition point to be determined between different types of signals with greater accuracy, resulting successful versus unsuccessful prediction of seizure. The theoretical contribution of the study may provide an opportunity for the development of a clinical device to predict forthcoming seizure in real time.

View PDFchevron_right
Seizure prediction: The Fourth International Workshop
Susan Arthurs

Epilepsy & Behavior, 2010

The recently convened Fourth International Workshop on Seizure Prediction (IWSP4) brought together a diverse international group of investigators, from academia and industry, including epileptologists, neurosurgeons, neuroscientists, computer scientists, engineers, physicists, and mathematicians who are conducting interdisciplinary research on the prediction and control of seizures. IWSP4 allowed the presentation and discussion of results, an exchange of ideas, an assessment of the status of seizure prediction, control and related fields and the fostering of collaborative projects.

View PDFchevron_right
Seizure prediction
J. Chris Sackellares

Epilepsy currents / American Epilepsy Society

There is mounting evidence that seizures are preceded by characteristic changes in the EEG that are detectable minutes before seizure onset. Using novel signal analysis techniques, researchers are beginning to characterize the transition from the interictal to the ictal state in quantitative terms. This research has led to the development of automated seizure prediction algorithms. Active debate persists regarding the interpretation of research results, methods of signal analysis, as well as experimental and statistical methods for testing seizure prediction algorithms. Developments in this field have led to new theories on the mechanism of seizure development and resolution. The ability to predict seizures could lead the way to novel diagnostic and therapeutic methods for the treatment of patients with epilepsy.

View PDFchevron_right
The First International Collaborative Workshop on Seizure Prediction: summary and data description
Brian Litt

Clinical Neurophysiology, 2005

51 researchers from 16 centers in seven countries. There were four major goals for the workshop: (1) to host a one-day didactic session on the science of seizure prediction, with lectures by leaders in the field; (2) to assess the current state of the field by applying current methods used to predict seizures to a shared set of continuous intracranial EEG data and discussing the strengths and weaknesses of each approach; (3) to establish a consensus on minimal data requirements, a common nomenclature, and objective methods for comparing system performance across platforms and laboratories for seizure prediction research; and most importantly (4) to establish a multi-laboratory, international working group dedicated to understanding seizure generation and making on-line, prospective seizure prediction a reality. Following the didactic course, each participating group presented their results, after applying their seizure prediction methods to five common data sets agreed upon in advance and distributed before the meeting. What follows is a description of the shared data set used for analysis, a summary of the major discussion points from the workshop, and points of consensus among the group. The brief discussion serves as a common introduction to the research papers that follow in this issue, and the description of the shared data is referenced in each of these papers. Participants in the workshop are listed at the end of the Conclusions section, in alphabetical order.

View PDFchevron_right
Seizure Prediction Methods: A Review of the Current Predicting Techniques
mostafa jalilifar

Biomedical & Pharmacology Journal, 2014

Epilepsy is a common neurological disorder affecting more than 1.5 percent worldwide. Twenty percent of epilepsies are drug resistant. Therefore, early detection of prediction of epileptic seizures is of prime significance to decrease the burdens of the disease. There is strong evidence indicating seizures develop minutes to hours before clinical onset. This change is based on quantitative studies of long term electroencephalographic monitoring (EEG) from patients administered for epilepsy surgery. The possibility of early prediction of seizure has drawn the research interest of diverse fields in medical, engineering, and patent publications. Techniques used to predict seizures include frequency-based methods, statistical analysis of EEG signals, non-linear dynamics (chaos), and intelligent expert systems. Developing efficient methods to predict seizures can lead to designing novel diagnostic and therapeutic techniques for the early diagnosis of seizure attack or preventing the attacks through appropriate modulations of brain activities. The present study reviews the most important and recent methods for seizure prediction. In line with introduction of different efficient seizure predicting approaches, great research interest has been focused on developing new modalities that incorporate these approaches to predict early onset of seizures minutes to hours before they initiate. These modalities will enable experts to develop new interventional treatments such as appropriate responsive electric stimulation applied immediately after the prediction to prevent the seizures or modulating stimulation in controlling the seizure attacks. In the near future, seizures can be predicted in time and prevented before clinical and physical indications.

View PDFchevron_right
Seizure prediction: The impact of long prediction horizons
Andreas Schulze-bonhage, J. Timmer

Epilepsy Research, 2007

Several procedures have been proposed to be capable of predicting the occurrence of epileptic seizures. Up to now, all proposed algorithms are far from being sufficient for a clinical application. This is, however, often not obvious when results of seizure prediction performance are reported. Here, we discuss impacts of long prediction horizons with respect to clinical needs and the strain on patients by analyzing long-term continuous intracranial electroencephalography data.

View PDFchevron_right
Seizure Prediction: Its Evolution and Therapeutic Potential
Brian Litt

Blue Books of Neurology, 2009

View PDFchevron_right
Prediction of epileptic seizures
Javier Echauz

2002

View PDFchevron_right
Seizure prediction: science fiction or soon to become reality?
Philippa Karoly, Andre Peterson, Levin Kuhlmann

Curr Neurol Neurosci Rep, 2015

This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this we present a review of developments in high-resolution microelectrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of seizure prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled seizures. However, a missing piece of the puzzle is devices to acquire long-term high quality data. When this void is filled, seizure prediction will become a reality.

View PDFchevron_right

References (244)

  1. Banerjee, P. N., Filippi, D. & Allen Hauser, W. The descriptive epidemiology of epilepsy-a review. Epilepsy Res. 85, 31-45, (2009).
  2. Kwan, P., Schachter, S. C. & Brodie, M. J. Drug-resistant epilepsy. N. Engl. J. Med. 365, 919-926, (2011).
  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, e271-278, (2011).
  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).
  5. Epilepsy-Foundation. Ei2 community survey. 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).
  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).
  8. Mormann, F., Andrzejak, R. G., Elger, C. E. & Lehnertz, K. Seizure prediction: the long and winding road. Brain 130, 314-333, (2007).
  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).
  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).
  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).
  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).
  13. Kuhlmann, L. et al. Epilepsyecosystem.org: Crowd-Sourcing Reproducible Seizure Prediction with Long-Term Human Intracranial EEG. Brain, In Press, (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).
  15. Karoly, P. J. et al. The circadian profile of epilepsy improves seizure forecasting. Brain 140, 2169-2182, (2017).
  16. Kiral-Kornek, I. et al. Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System. Ebiomedicine 27, 103-111, (2018).
  17. Truong, N. D. et al. Convolutional Neural Network for Seizure Prediction using Intracranial and Scalp Electroencephalogram. Neural Netw., In Press, (2018).
  18. Kuhlmann, L. et al. Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons. Epi. Res. 91, 214-231, (2010).
  19. Schelter, B. et al. Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction. Chaos 16, 013108, (2006).
  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).
  21. Andrzejak, R. G., Chicharro, D., Elger, C. E. & Mormann, F. Seizure prediction: any better than chance? Clin. Neurophysiol. 120, 1465-1478, (2009).
  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).
  23. Lehnertz, K. & Litt, B. The First International Collaborative Workshop on Seizure Prediction: summary and data description. Clin. Neurophysiol. 116, 493-505, (2005).
  24. Ihle, M. et al. EPILEPSIAE-A European epilepsy database. Comput. Methods Programs Biomed. 106, 127-138, (2012).
  25. Klatt, J. et al. The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients. Epilepsia 53, 1669-1676, (2012).
  26. Wagenaar, J. B. et al. Collaborating and sharing data in epilepsy research. J. Clin. Neurophysiol. 32, 235-239, (2015).
  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).
  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).
  29. Freestone, D. R., Karoly, P. J. & Cook, M. J. A forward-looking review of seizure prediction. Curr. Opin. Neurol. 30, 167-173, (2017).
  30. Cook, M. J. et al. The dynamics of the epileptic brain reveal long-memory processes. Front. Neurol. 5, 217, (2014).
  31. Cook, M. J. et al. Human focal seizures are characterized by populations of fixed duration and interval. Epilepsia 57, 359-368, (2016).
  32. Gluckman, B. J. & Schevon, C. A. Seizure Prediction 6: From Mechanisms to Engineered Interventions for Epilepsy. J. Clin. Neurophysiol. 32, 181-187, (2015).
  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).
  34. Osorio, I., Zaveri, H. P., Frei, M. G. & Arthurs, S. in 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).
  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. in 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).
  39. Brinkmann, B. H. et al. Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713-1722, (2016).
  40. Howbert, J. J. et al. Forecasting seizures in dogs with naturally occurring epilepsy. PLoS One 9, e81920, (2014).
  41. Karoly, P. J. et al. Interictal spikes and epileptic seizures: their relationship and underlying rhythmicity. Brain 139, 1066-1078, (2016).
  42. Payne, D. E. et al. Postictal suppression and seizure durations: A patient-specific, long- term iEEG analysis. Epilepsia 59, 1027-1036, (2018).
  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).
  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).
  45. Gotman, J. Relationships between Interictal Spiking and Seizures -Human and Experimental-Evidence. Can. J. Neurol. Sci. 18, 573-576, (1991).
  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).
  47. Li, S. F., Zhou, W. D., Yuan, Q. & Liu, Y. X. Seizure Prediction Using Spike Rate of Intracranial EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 21, 880-886, (2013).
  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).
  49. Goncharova, II et al. The relationship between seizures, interictal spikes and antiepileptic drugs. Clin. Neurophysiol. 127, 3180-3186, (2016).
  50. Goncharova, II, 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).
  51. Goncharova, II 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).
  52. Spencer, S. S., Goncharova, II, Duckrow, R. B., Novotny, E. J. & Zaveri, H. P. Interictal spikes on intracranial recording: behavior, physiology, and implications. Epilepsia 49, 1881- 1892, (2008).
  53. Abou-Khalil, B. The ambiguous relationship between spikes and seizures. Clin. Neurophysiol. 127, 3176-3177, (2016).
  54. Baud, M. O. et al. Multi-day rhythms modulate seizure risk in epilepsy. Nature communications 9, 88, (2018).
  55. Gotman, J. A few thoughts on ``What is a seizure?''. Epilepsy Behav. 22, S2-S3, (2011).
  56. Staley, K. Molecular mechanisms of epilepsy. Nat. Neurosci. 18, 367-372, (2015).
  57. Terry, J. R., Benjamin, O. & Richardson, M. P. Seizure generation: the role of nodes and networks. Epilepsia 53, e166-169, (2012).
  58. Blumenfeld, H. Cellular and network mechanisms of spike-wave seizures. Epilepsia 46 Suppl 9, 21-33, (2005).
  59. Schevon, C. A. et al. Cortical abnormalities in epilepsy revealed by local EEG synchrony. Neuroimage 35, 140-148, (2007).
  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).
  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).
  62. Palmigiano, A., Pastor, J., Garcia de Sola, R. & Ortega, G. J. Stability of synchronization clusters and seizurability in temporal lobe epilepsy. PLoS ONE [Electronic Resource] 7, e41799, (2012).
  63. Towle, V. L., Carder, R. K., Khorasani, L. & Lindberg, D. Electrocorticographic coherence patterns. J. Clin. Neurophysiol. 16, 528-547, (1999).
  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).
  65. Zaveri, H. P. et al. Localization-related epilepsy exhibits significant connectivity away from the seizure-onset area. Neuroreport 20, 891-895, (2009).
  66. Zaveri, H. P., Pincus, S. M., Goncharova, I. I., Duckrow, R. B. & Spencer, S. S. in Proc. of SPIE. 70740T-70741.
  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).
  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).
  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). 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).
  70. Sinha, N. et al. Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling. Brain 140, 319-332, (2017).
  71. Eissa, T. L. & Schevon, C. A. The role of computational modelling in seizure localization. Brain 140, 254-256, (2017).
  72. Lee, H. W. et al. Altered functional connectivity in seizure onset zones revealed by fMRI intrinsic connectivity. Neurology 83, 2269-2277, (2014).
  73. Constable, R. T. et al. Potential use and challenges of functional connectivity mapping in intractable epilepsy. Front. Neurol. 4, 39, (2013).
  74. 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).
  75. 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). Elisevich, K. et al. An assessment of MEG coherence imaging in the study of temporal lobe epilepsy. Epilepsia 52, 1110-1119, (2011).
  76. Nissen, I. A. et al. Identifying the epileptogenic zone in interictal resting-state MEG source- space networks. Epilepsia 58, 137-148, (2017).
  77. Englot, D. J. et al. Epileptogenic zone localization using magnetoencephalography predicts seizure freedom in epilepsy surgery. Epilepsia 56, 949-958, (2015).
  78. Englot, D. J. et al. Global and regional functional connectivity maps of neural oscillations in focal epilepsy. Brain 138, 2249-2262, (2015).
  79. 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).
  80. 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).
  81. 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). Sunderam, S., Osorio, I. & Frei, M. G. Epileptic seizures are temporally interdependent under certain conditions. Epilepsy Res. 76, 77-84, (2007).
  82. 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).
  83. 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).
  84. Staniek, M. & Lehnertz, K. Symbolic transfer entropy: inferring directionality in biosignals. Biomed. Tech. (Berl.) 54, 323-328, (2009).
  85. 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).
  86. Dickten, H. & Lehnertz, K. Identifying delayed directional couplings with symbolic transfer entropy. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 90, 062706, (2014).
  87. Lehnertz, K. & Dickten, H. Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients. Philos T R Soc A 373, 20140094, (2015). 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).
  88. Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186-198, (2009).
  89. Richardson, M. Current themes in neuroimaging of epilepsy: brain networks, dynamic phenomena, and clinical relevance. Clin. Neurophysiol. 121, 1153-1175, (2010).
  90. Kramer, M. A. & Cash, S. S. Epilepsy as a Disorder of Cortical Network Organization. Neuroscientist 18, 360-372, (2012).
  91. 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). Lehnertz, K. et al. Evolving networks in the human epileptic brain. Physica D-Nonlinear Phenomena 267, 7-15, (2014).
  92. Stam, C. J. Modern network science of neurological disorders. Nat. Rev. Neurosci. 15, 683- 695, (2014).
  93. Yaffe, R. B. et al. Physiology of functional and effective networks in epilepsy. Clin. Neurophysiol. 126, 227-236, (2015).
  94. 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).
  95. 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, S144-152, (2000).
  96. Spencer, S. S. Neural networks in human epilepsy: evidence of and implications for treatment. Epilepsia 43, 219-227, (2002).
  97. 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). Schindler, K., Elger, C. E. & LehnertZ, K. Increasing synchronization may promote seizure termination: Evidence from status epilepticus. Clin. Neurophysiol. 118, 1955-1968, (2007).
  98. Kramer, M. A., Kolaczyk, E. D. & Kirsch, H. E. Emergent network topology at seizure onset in humans. Epilepsy Res. 79, 173-186, (2008).
  99. 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).
  100. Valton, L. et al. Functional interactions in brain networks underlying epileptic seizures in bilateral diffuse periventricular heterotopia. Clin. Neurophysiol. 119, 212-223, (2008).
  101. Kramer, M. A. et al. Coalescence and fragmentation of cortical networks during focal seizures. J. Neurosci. 30, 10076-10085, (2010).
  102. Bialonski, S., Wendler, M. & Lehnertz, K. Unraveling spurious properties of interaction networks with tailored random networks. PLoS One 6, e22826, (2011).
  103. Kramer, M. A. et al. Human seizures self-terminate across spatial scales via a critical transition. Proc. Natl. Acad. Sci. U. S. A. 109, 21116-21121, (2012). 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).
  104. Liao, W. et al. Dynamical intrinsic functional architecture of the brain during absence seizures. Brain Struct Funct 219, 2001-2015, (2014).
  105. Afra, P., Jouny, C. C. & Bergey, G. K. Termination patterns of complex partial seizures: An intracranial EEG study. Seizure 32, 9-15, (2015).
  106. Geier, C., Bialonski, S., Elger, C. E. & Lehnertz, K. How important is the seizure onset zone for seizure dynamics? Seizure 25, 160-166, (2015).
  107. 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).
  108. 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).
  109. 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).
  110. 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). Geier, C. & Lehnertz, K. Long-term variability of importance of brain regions in evolving epileptic brain networks. Chaos 27, 043112, (2017).
  111. Burns, S. P. et al. Network dynamics of the brain and influence of the epileptic seizure onset zone. Proc. Natl. Acad. Sci. U. S. A. 111, E5321-5330, (2014).
  112. Kuhnert, M. T., Elger, C. E. & Lehnertz, K. Long-term variability of global statistical properties of epileptic brain networks. Chaos 20, 043126, (2010).
  113. 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).
  114. 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).
  115. Deisseroth, K. & Schnitzer, M. J. Engineering approaches to illuminating brain structure and dynamics. Neuron 80, 568-577, (2013).
  116. 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).
  117. Stead, M. et al. Microseizures and the spatiotemporal scales of human partial epilepsy. Brain 133, 2789-2797, (2010).
  118. Truccolo, W. et al. Single-neuron dynamics in human focal epilepsy. Nat. Neurosci. 14, 635-641, (2011).
  119. Schevon, C. A. et al. Evidence of an inhibitory restraint of seizure activity in humans. Nature communications 3, 1060, (2012).
  120. Hu, S. Q. et al. Increase trend of correlation and phase synchrony of microwire iEEG before macroseizure onset. Cogn. Neurodyn. 8, 111-126, (2014).
  121. 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).
  122. Smith, E. H. et al. The ictal wavefront is the spatiotemporal source of discharges during spontaneous human seizures. Nature communications 7, 11098, (2016).
  123. Petroff, O. A. et al. Glutamate-glutamine cycling in the epileptic human hippocampus. Epilepsia 43, 703-710, (2002).
  124. Cavus, I. et al. Extracellular metabolites in the cortex and hippocampus of epileptic patients. Ann. Neurol. 57, 226-235, (2005).
  125. 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).
  126. Stephens, M. L. et al. Tonic glutamate in CA1 of aging rats correlates with phasic glutamate dysregulation during seizure. Epilepsia 55, 1817-1825, (2014).
  127. 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).
  128. 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).
  129. During, M. J. & Spencer, D. D. Extracellular hippocampal glutamate and spontaneous seizure in the conscious human brain. Lancet 341, 1607-1610, (1993).
  130. Huberfeld, G. et al. Glutamatergic pre-ictal discharges emerge at the transition to seizure in human epilepsy. Nat. Neurosci. 14, 627-U121, (2011).
  131. 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).
  132. Smith, E. H. et al. The ictal wavefront is the spatiotemporal source of discharges during spontaneous human seizures. Nature communications 7, 11098, (2016).
  133. 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).
  134. 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).
  135. Patel, K. S., Zhao, M., Ma, H. & Schwartz, T. H. Imaging preictal hemodynamic changes in neocortical epilepsy. Neurosurg. Focus 34, E10, (2013).
  136. 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). 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).
  137. Bruno, E., Biondi, A., Richardson, M. P. & RADAR-CNS-Consortium. Pre-ictal heart rate changes: A systematic review and meta-analysis. Seizure 55, 48-56, (2018).
  138. 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).
  139. 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).
  140. Moeller, F. et al. Simultaneous EEG-fMRI in drug-naive children with newly diagnosed absence epilepsy. Epilepsia 49, 1510-1519, (2008).
  141. 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).
  142. Carney, P. W. et al. The core network in absence epilepsy. Differences in cortical and thalamic BOLD response. Neurology 75, 904-911, (2010).
  143. Moeller, F. et al. Changes in activity of striato-thalamo-cortical network precede generalized spike wave discharges. Neuroimage 39, 1839-1849, (2008).
  144. 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).
  145. 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).
  146. 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).
  147. 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).
  148. 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).
  149. 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).
  150. 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. (IEEE. p. 6428-6431).
  151. 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).
  152. 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, S102-109, (2011).
  153. 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).
  154. Freestone, D. R. et al. Electrical probing of cortical excitability in patients with epilepsy. Epilepsy Behav. 22 Suppl 1, S110-118, (2011).
  155. 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. U. S. A. 112, 14694-14699, (2015).
  156. Lytton, W. W. Computer modelling of epilepsy. Nat. Rev. Neurosci. 9, 626-637, (2008).
  157. Soltesz, I. & Staley, K. Computational neuroscience in epilepsy. Vol. 6 (Academic Press, 2008). Stefanescu, R. A., Shivakeshavan, R. G. & Talathi, S. S. Computational models of epilepsy. Seizure 21, 748-759, (2012).
  158. Volman, V., Bazhenov, M. & Sejnowski, T. J. Computational models of neuron-astrocyte interaction in epilepsy. Front. Comput. Neurosci. 6, 58, (2012).
  159. Holt, A. B. & Netoff, T. I. Computational modeling of epilepsy for an experimental neurologist. Exp. Neurol. 244, 75-86, (2013).
  160. Wendling, F., Benquet, P., Bartolomei, F. & Jirsa, V. Computational models of epileptiform activity. J. Neurosci. Methods 260, 233-251, (2016).
  161. Chakravarthy, N., Sabesan, S., Tsakalis, K. & Iasemidis, L. Controlling epileptic seizures in a neural mass model. Journal of Combinatorial Optimization 17, 98-116, (2009).
  162. 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).
  163. 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).
  164. Freestone, D. et al. Patient-specific neural mass modelling: stochastic and deterministic methods. Recent Advances in Predicting and Preventing Epileptic Seizures, 63-82, (2013).
  165. Freestone, D. R. et al. Seizure Prediction: Science Fiction or Soon to Become Reality? Curr. Neurol. Neurosci. Rep. 15, 73, (2015).
  166. 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).
  167. Watson, P. D., Horecka, K. M., Cohen, N. J. & Ratnam, R. A Phase-Locked Loop Epilepsy Network Emulator. Neurocomputing 173, 1245-1249, (2016).
  168. 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).
  169. 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).
  170. Rabinovich, M. I., Varona, P., Selverston, A. I. & Abarbanel, H. D. I. Dynamical principles in neuroscience. Rev Mod Phys 78, 1213-1265, (2006).
  171. 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). Milton, J. G. Neuronal avalanches, epileptic quakes and other transient forms of neurodynamics. Eur. J. Neurosci. 36, 2156-2163, (2012).
  172. 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).
  173. 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). 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).
  174. 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).
  175. 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). Rothkegel, A. & Lehnertz, K. Recurrent events of synchrony in complex networks of pulse- coupled oscillators. Epl-Europhys Lett 95, 38001, (2011).
  176. 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).
  177. 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). 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).
  178. 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). Ansmann, G., Lehnertz, K. & Feudel, U. Self-induced pattern switching on complex networks of excitable units. Phys. Rev. X 6, 011030, (2016).
  179. Schiff, S. J. Neural control engineering: the emerging intersection between control theory and neuroscience. (MIT Press, 2012).
  180. Chong, M. S., Nešić, D., Postoyan, R. & Kuhlmann, L. Parameter and state estimation of nonlinear systems using a multi-observer under the supervisory framework. IEEE Transactions on Automatic Control 60, 2336-2349, (2015).
  181. Kuhlmann, L. et al. Neural mass model-based tracking of anesthetic brain states. Neuroimage 133, 438-456, (2016).
  182. Xian, L., Qing, G. & Xiao-Li, L. Control of epileptiform spikes based on nonlinear unscented Kalman filter. Chinese Physics B 23, 010202, (2013).
  183. Taylor, P. N. et al. Optimal control based seizure abatement using patient derived connectivity. Front. Neurosci. 9, 202, (2015).
  184. Wei, Y., Ullah, G. & Schiff, S. J. Unification of neuronal spikes, seizures, and spreading depression. J. Neurosci. 34, 11733-11743, (2014).
  185. De Ciantis, A. & Lemieux, L. Localisation of epileptic foci using novel imaging modalities. Curr. Opin. Neurol. 26, 368-373, (2013).
  186. Krook-Magnuson, E., Armstrong, C., Oijala, M. & Soltesz, I. On-demand optogenetic control of spontaneous seizures in temporal lobe epilepsy. Nature communications 4, 1376, (2013). Badawy, R. A., Freestone, D. R., Lai, A. & Cook, M. J. Epilepsy: Ever-changing states of cortical excitability. Neuroscience 222, 89-99, (2012).
  187. Bazaka, K. & Jacob, M. V. Implantable devices: issues and challenges. Electronics 2, 1-34, (2012). Jory, C. et al. Safe and sound? A systematic literature review of seizure detection methods for personal use. Seizure 36, 4-15, (2016).
  188. Patel, A. D. et al. Patient-centered design criteria for wearable seizure detection devices. Epilepsy Behav. 64, 116-121, (2016).
  189. Ramgopal, S. et al. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav. 37, 291-307, (2014).
  190. 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).
  191. Arthurs, S., Zaveri, H. P., Frei, M. G. & Osorio, I. Patient and caregiver perspectives on seizure prediction. Epilepsy Behav. 19, 474-477, (2010).
  192. Schulze-Bonhage, A. et al. Views of patients with epilepsy on seizure prediction devices. Epilepsy Behav. 18, 388-396, (2010).
  193. Hoppe, C. et al. Novel techniques for automated seizure registration: Patients' wants and needs. Epilepsy Behav. 52, 1-7, (2015).
  194. Hoppe, C., Poepel, A. & Elger, C. E. Epilepsy: accuracy of patient seizure counts. Arch. Neurol. 64, 1595-1599, (2007).
  195. Blachut, B. et al. Counting seizures: The primary outcome measure in epileptology from the patients' perspective. Seizure 29, 97-103, (2015).
  196. Elger, C. E. & Hoppe, C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol. 17, 279-288, (2018).
  197. 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., 1-13, (2018).
  198. Vandecasteele, K. et al. Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment. Sensors (Basel) 17, 2338, (2017).
  199. Gadhoumi, K., Lina, J. M., Mormann, F. & Gotman, J. Seizure prediction for therapeutic devices: A review. J. Neurosci. Methods 260, 270-282, (2016).
  200. Fisher, R. et al. Electrical stimulation of the anterior nucleus of thalamus for treatment of refractory epilepsy. Epilepsia 51, 899-908, (2010).
  201. 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).
  202. Yuan, H. & Silberstein, S. D. Vagus Nerve and Vagus Nerve Stimulation, a Comprehensive Review: Part II. Headache 56, 259-266, (2016).
  203. 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).
  204. Stacey, W. C. & Litt, B. Technology insight: neuroengineering and epilepsy-designing devices for seizure control. Nature Clinical Practice Neurology 4, 190-201, (2008).
  205. Brinkmann, B. H. et al. Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy. PLoS One 10, e0133900, (2015).
  206. 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). Gadhoumi, K., Lina, J. M. & Gotman, J. Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity. Clin. Neurophysiol. 124, 1745-1754, (2013). Senger, V. & Tetzlaff, R. New Signal Processing Methods for the Development of Seizure Warning Devices in Epilepsy. IEEE Transactions. on Circuits and Systems I: Regular Papers 63, 609-616, (2016).
  207. Zheng, Y., Wang, G., Li, K., Bao, G. & Wang, J. Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition. Clin. Neurophysiol. 125, 1104-1111, (2014).
  208. Aarabi, A. & He, B. A rule-based seizure prediction method for focal neocortical epilepsy. Clin. Neurophysiol. 123, 1111-1122, (2012).
  209. Park, Y., Luo, L., Parhi, K. K. & Netoff, T. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 52, 1761-1770, (2011).
  210. Williamson, J. R., Bliss, D. W., Browne, D. W. & Narayanan, J. T. Seizure prediction using EEG spatiotemporal correlation structure. Epilepsy Behav. 25, 230-238, (2012). Zhang, Z. & Parhi, K. K. Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power. IEEE Trans. Biomed. Circ. Syst. 10, 693- 706, (2015).
  211. Mirowski, P., Madhavan, D., Lecun, Y. & Kuzniecky, R. Classification of patterns of EEG synchronization for seizure prediction. Clin. Neurophysiol. 120, 1927-1940, (2009).
  212. Moghim, N. & Corne, D. W. Predicting epileptic seizures in advance. PloS one 9, e99334, (2014). Eftekhar, A., Juffali, W., El-Imad, J., Constandinou, T. G. & Toumazou, C. Ngram-derived pattern recognition for the detection and prediction of epileptic seizures. PloS one 9, e96235, (2014).
  213. Aarabi, A. & He, B. Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach. Clin. Neurophysiol. 125, 930-940, (2014).
  214. Li, S. F., Zhou, W. D., Yuan, Q. & Liu, Y. X. Seizure Prediction Using Spike Rate of Intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering 21, 880-886, (2013).
  215. Zhang, Z. & Parhi, K. K. in 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). (IEEE. p. 5748-5751).
  216. Zandi, A. S., Tafreshi, R., Javidan, M. & Dumont, G. A. Predicting epileptic seizures in scalp EEG based on a variational Bayesian Gaussian mixture model of zero-crossing intervals. IEEE Trans. Biomed. Eng. 60, 1401-1413, (2013).
  217. Chien, J.-H. et al. A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings. Cybern. Syst. Anal. 47, 586-597, (2011).
  218. 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).
  219. Kiral-Kornek, I. et al. Epileptic seizure prediction using big data and deep learning: toward a mobile system. EBioMedicine 27, 103-111 (2018).
  220. Karoly, P. et al. The Circadian Profile of Epilepsy Improves Seizure Forecasting. Brain 140, 2169-2182 (2017).
  221. Kuhlmann, L. et al. Epilepsyecosystem.org: Crowd-Sourcing Reproducible Seizure Prediction with Long-Term Human Intracranial EEG. Brain In Press (2018).
  222. Feldwisch-Drentrup, H. et al. Joining the benefits: combining epileptic seizure prediction methods. Epilepsia 51, 1598-1606 (2010).
  223. Wang, S., Chaovalitwongse, W. A. & Wong, S. Online seizure prediction using an adaptive learning approach. IEEE Trans. Know. Data Eng. 25, 2854-2866 (2013).
  224. Xiao, C., Wang, S., Iasemidis, L., Wong, S. & Chaovalitwongse, W. A. An Adaptive Pattern Learning Framework to Personalize Online Seizure Prediction. IEEE Trans. Big Data, doi:10.1109/TBDATA.2017.2675982 (2017).
  225. Sackellares, J. C. et al. Predictability analysis for an automated seizure prediction algorithm. J. Clin. Neurophysiol. 23, 509-520 (2006).
  226. Kuhlmann, L. et al. Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons. Epilepsy Res. 91, 214-231 (2010).
  227. Alvarado-Rojas, C. et al. Slow modulations of high-frequency activity (40-140 Hz) discriminate preictal changes in human focal epilepsy. Sci. Rep. 4 (2014).
  228. Teixeira, C. A. et al. Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Comput. Methods Programs Biomed. 114, 324-336 (2014).
  229. Valderrama, M. et al. Identifying an increased risk of epileptic seizures using a multi-feature EEG-ECG classification. Biomed. Sig. Proc. Control 7, 237-244 (2012).
  230. Rasekhi, J., Mollaei, M. R. K., Bandarabadi, M., Teixeira, C. A. & Dourado, A. Epileptic seizure prediction based on ratio and differential linear univariate features. J. Med. Sig. Sens. 5, 1 (2015).
  231. Rasekhi, J., Mollaei, M. R. K., Bandarabadi, M., Teixeira, C. A. & Dourado, A. Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods. J. Neurosci. Methods 217, 9-16 (2013).
  232. Bandarabadi, M., Teixeira, C. A., Rasekhi, J. & Dourado, A. Epileptic seizure prediction using relative spectral power features. Clin. Neurophysiol. 126, 237-248 (2015).
  233. Direito, B., Teixeira, C. A., Sales, F., Castelo-Branco, M. & Dourado, A. A Realistic Seizure Prediction Study Based on Multiclass SVM. Int. J. Neural Syst. 27, 1750006 (2017).
  234. 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). Gadhoumi, K., Lina, J.-M. & Gotman, J. Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity. Clin. Neurophysiol. 124, 1745-1754 (2013). Senger, V. & Tetzlaff, R. New Signal Processing Methods for the Development of Seizure Warning Devices in Epilepsy. IEEE Trans. Circuits Syst. I: Regular Papers 63, 609-616 (2016). Zheng, Y., Wang, G., Li, K., Bao, G. & Wang, J. Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition. Clin. Neurophysiol. 125, 1104-1111 (2014).
  235. Aarabi, A. & He, B. A rule-based seizure prediction method for focal neocortical epilepsy. Clin. Neurophysiol. 123, 1111-1122 (2012).
  236. Park, Y., Luo, L., Parhi, K. K. & Netoff, T. Seizure prediction with spectral power of EEG using cost sensitive support vector machines. Epilepsia 52, 1761-1770 (2011).
  237. Williamson, J. R., Bliss, D. W., Browne, D. W. & Narayanan, J. T. Seizure prediction using EEG spatiotemporal correlation structure. Epilepsy Behav. 25, 230-238 (2012). Zhang, Z. & Parhi, K. K. Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power. IEEE Trans. Biomed. Circ. Syst. 10, 693-706 (2015).
  238. Mirowski, P., Madhavan, D., Lecun, Y. & Kuzniecky, R. Classification of patterns of EEG synchronization for seizure prediction. Clin. Neurophysiol. 120, 1927-1940 (2009).
  239. Moghim, N. & Corne, D. W. Predicting epileptic seizures in advance. PloS one 9, e99334 (2014). Eftekhar, A., Juffali, W., El-Imad, J., Constandinou, T. G. & Toumazou, C. Ngram-derived pattern recognition for the detection and prediction of epileptic seizures. PloS one 9, e96235 (2014).
  240. Aarabi, A. & He, B. Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach. Clin. Neurophysiol. 125, 930-940 (2014).
  241. 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).
  242. Zhang, Z. & Parhi, K. K. in 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). (IEEE. p. 5748-5751).
  243. Zandi, A. S., Tafreshi, R., Javidan, M. & Dumont, G. A. Predicting epileptic seizures in scalp EEG based on a variational Bayesian Gaussian mixture model of zero-crossing intervals. IEEE Trans. Biomed. Eng. 60, 1401-1413 (2013).
  244. Chien, J.-H. et al. A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings. Cybern. Syst. Anal. 47, 586-597 (2011).

Related papers

Seizure prediction: the long and winding road
Florian Mormann

Brain, 2007

The sudden and apparently unpredictable nature of seizures is one of the most disabling aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures from the electroencephalogram (EEG) of epilepsy patients would open new therapeutic possibilities. Since the 1970s investigations on the predictability of seizures have advanced from preliminary descriptions of seizure precursors to controlled studies applying prediction algorithms to continuous multi-day EEG recordings. While most of the studies published in the 1990s and around the turn of the millennium yielded rather promising results, more recent evaluations could not reproduce these optimistic findings, thus raising a debate about the validity and reliability of previous investigations. In this review, we will critically discuss the literature on seizure prediction and address some of the problems and pitfalls involved in the designing and testing of seizure-prediction algorithms. We will give an account of the current state of this research field, point towards possible future developments and propose methodological guidelines for future studies on seizure prediction.

View PDFchevron_right
Special issue on epileptic seizure prediction
Brian Litt

IEEE Transactions on Biomedical Engineering, 2003

E PILEPSY is the most common neurological disorder after stroke, and affects almost 60 million people worldwide. Medications control seizures in only 2/3 of those affected, and another 7%-8% are potentially curable by surgery. This leaves fully 25%, or 15 million people whose seizures cannot be controlled by any available therapy. Over the past ten years engineers and quantitative scientists have amassed evidence that seizures do not begin abruptly, as was previously thought, but develop over time, even hours before they cause clinical symptoms. This discovery leads to the exciting notion that there might be time to understand the physiological events that give rise to seizures and perhaps alter them before they can disrupt normal function. Collaborations between engineers, physicists, mathematicians, clinicians, neuroscientists, and industry are focusing on how to translate this work into therapeutic devices that can predict and modulate epileptic seizures, as well as how to explore these findings in the hope of understanding how seizures are generated in the brain and the underlying cellular mechanisms that drive this process. These collaborative efforts may eventually lead to triggering therapy, such as local electrical or magnetic stimulation, drug infusion, or cooling, by preventing the onset or limiting the spread of seizures. Work on these interventions is still early in its development, but a wave of gathering interest is propelling the field in a variety of directions, efforts that are mostly fueled by bioengineering technology.

View PDFchevron_right
Long-term prospective on-line real-time seizure prediction. Clin Neurophysiol 116: 532-544
Konstantinos Tsakalis

2005

Objective: Epilepsy, one of the most common neurological disorders, constitutes a unique opportunity to study the dynamics of spatiotemporal state transitions in real, complex, nonlinear dynamical systems. In this study, we evaluate the performance of a prospective on-line real-time seizure prediction algorithm in two patients from a common database. Methods: We previously demonstrated that measures of chaos and angular frequency, estimated from electroencephalographic (EEG) signals recorded at critical sites in the cerebral cortex, progressively converge (i.e. become dynamically entrained) as the epileptic brain transits from the asymptomatic interictal state to the ictal state (seizure) (Iasemidis et al., 2001, 2002a, 2003a). This observation suggested the possibility of developing algorithms to predict seizures well ahead of their occurrences. One of the central points in those investigations was the application of optimization theory, specifically quadratic zero-one programming, for the selection of the critical cortical sites. This current study combines that observation with a dynamical entrainment detection method to prospectively predict epileptic seizures. The algorithm was tested in two patients with long-term (107.54 h) and multi-seizure EEG data B and C (Lehnertz and Litt, 2004). Results: Analysis from the 2 test patients resulted in the prediction of up to 91.3% of the impending 23 seizures, about 89G15 min prior to seizure onset, with an average false warning rate of one every 8.27 h and an allowable prediction horizon of 3 h. Conclusions: The algorithm provides warning of impending seizures prospectively and in real time, that is, it constitutes an on-line and real-time seizure prediction scheme. Significance: These results suggest that the proposed seizure prediction algorithm could be used in novel diagnostic and therapeutic applications in epileptic patients.

View PDFchevron_right
On the predictability of epileptic seizures
Florian Mormann

Clinical Neurophysiology, 2005

Objective: An important issue in epileptology is the question whether information extracted from the EEG of epilepsy patients can be used for the prediction of seizures. Several studies have claimed evidence for the existence of a pre-seizure state that can be detected using different characterizing measures. In this paper, we evaluate the predictability of seizures by comparing the predictive performance of a variety of univariate and bivariate measures comprising both linear and non-linear approaches.

View PDFchevron_right
Towards accurate prediction of epileptic seizures: A review
Sandy Rihana

Biomedical Signal Processing and Control, 2017

Recent research has investigated the possibility of predicting epileptic seizures. Intervention before the onset of seizure manifestations could be envisioned with accurate seizure forecasting. Although efforts for better prediction have been made, the translation of current approaches to clinical applications is still not possible. While early findings have been optimistic, the absence of statistical validation and reproducibility has raised doubts about the existence of a preictal state. Analysis and algorithmic studies are providing evidence that transition to the ictal state is not random, with build-up leading to seizures. We have reviewed the general framework of reliable algorithmic seizure prediction studies, discussing each component of the whole block diagram. We have explored steps along the pathway, from signal acquisition to adequate performance evaluation that should be taken into account in the design of an efficient seizure advisory/intervention system. The present review has established that there is potential for improvement and optimization in the seizure prediction framework. New databases, higher sampling frequencies, adequate preprocessing, electrode selection, and machine-learning considerations are all elements of the prediction scheme that should be assessed to achieve more realistic, better-than-chance performances.

View PDFchevron_right

Cited by

Statistical Properties and Predictability of Extreme Epileptic Events
Vadim Grubov

Scientific Reports

the use of extreme events theory for the analysis of spontaneous epileptic brain activity is a relevant multidisciplinary problem. It allows deeper understanding of pathological brain functioning and unraveling mechanisms underlying the epileptic seizure emergence along with its predictability. the latter is a desired goal in epileptology which might open the way for new therapies to control and prevent epileptic attacks. With this goal in mind, we applied the extreme event theory for studying statistical properties of electroencephalographic (eeG) recordings of WAG/Rij rats with genetic predisposition to absence epilepsy. our approach allowed us to reveal extreme events inherent in this pathological spiking activity, highly pronounced in a particular frequency range. the return interval analysis showed that the epileptic seizures exhibit a highly-structural behavior during the active phase of the spiking activity. Obtained results evidenced a possibility for early (up to 7 s) prediction of epileptic seizures based on consideration of eeG statistical properties.

View PDFchevron_right
Prediction and detection of human epileptic seizures based on SIFT-MS chemometric data
Leticia Vitola Pasetto

Scientific Reports

Although epilepsy is considered a public health issue, the burden imposed by the unpredictability of seizures is mainly borne by the patients. Predicting seizures based on electroencephalography has had mixed success, and the idiosyncratic character of epilepsy makes a single method of detection or prediction for all patients almost impossible. To address this problem, we demonstrate herein that epileptic seizures can not only be detected by global chemometric analysis of data from selected ion flow tube mass spectrometry but also that a simple mathematical model makes it possible to predict these seizures (by up to 4 h 37 min in advance with 92% and 75% of samples correctly classified in training and leave-one-out-cross-validation, respectively). These findings should stimulate the development of non-invasive applications (e.g., electronic nose) for different types of epilepsy and thereby decrease of the unpredictability of epileptic seizures.

View PDFchevron_right
Noise amplification precedes extreme epileptic events on human EEG
Denis Andrikov

Physical Review E

Extreme events are rare and sudden abnormal deviations of the system's behavior from a typical state. Statistical analysis reveals that if the time series contains extreme events, its distribution has a heavy tail. In dynamical systems, extreme events often occur due to developing instability preceded by noise amplification. Here, we apply this theory to analyze generalized epileptic seizures in the human brain. First, we demonstrate that the time series of electroencephalogram (EEG) spectral power in a frequency band of 1-5 Hz obeys a heavy-tailed distribution, confirming the presence of extreme events. Second, we report that noise on EEG signals gradually increases before the seizure onset. Thus, we hypothesize that generalized epileptic seizures in humans are the extreme events emerging from instability accompanied by preictal noise amplification similar to other dynamical systems.

View PDFchevron_right
Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
Shreya Biswas

Applied Sciences

The novel SARS-CoV-2 virus, responsible for the dangerous pneumonia-type disease, COVID-19, has undoubtedly changed the world by killing at least 3,900,000 people as of June 2021 and compromising the health of millions across the globe. Though the vaccination process has started, in developing countries such as India, the process has not been fully developed. Thereby, a diagnosis of COVID-19 can restrict its spreading and level the pestilence curve. As the quickest indicative choice, a computerized identification framework ought to be carried out to hinder COVID-19 from spreading more. Meanwhile, Computed Tomography (CT) imaging reveals that the attributes of these images for COVID-19 infected patients vary from healthy patients with or without other respiratory diseases, such as pneumonia. This study aims to establish an effective COVID-19 prediction model through chest CT images using efficient transfer learning (TL) models. Initially, we used three standard deep learning (DL) mod...

View PDFchevron_right
Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy
Adriana Leal

Scientific Reports

Electrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure’s clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguis...

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved