title: Seizure prediction: methods.
abstract: Epilepsy, one of the most common neurological diseases, affects over 50 million people worldwide. Epilepsy can have a broad spectrum of debilitating medical and social consequences. Although antiepileptic drugs have helped treat millions of patients, roughly a third of all patients have seizures that are refractory to pharmacological intervention. The evolution of our understanding of this dynamic disease leads to new treatment possibilities. There is great interest in the development of devices that incorporate algorithms capable of detecting early onset of seizures or even predicting them hours before they occur. The lead time provided by these new technologies will allow for new types of interventional treatment. In the near future, seizures may be detected and aborted before physical manifestations begin. In this chapter we discuss the algorithms that make these devices possible and how they have been implemented to date. We also compare and contrast these measures, and review their individual strengths and weaknesses. Finally, we illustrate how these techniques can be combined in a closed-loop seizure prevention system. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
authors.affiliation: Departments of Pediatrics, Neurology, Neuroscience, and Biomedical Engineering, McKnight Brain Institute, Wilder Center of Excellence for Epilepsy Research, University of Florida College of Medicine, Gainesville, FL 32610–0296, USA. carnepr@peds.ufl.edu
authors.forename: Paul R
authors.initials: PR
authors.lastname: Carney
authors.forename: Stephen
authors.initials: S
authors.lastname: Myers
authors.forename: James D
authors.initials: JD
authors.lastname: Geyer
mesh_terms: D000465:Algorithms; D058256:Brain Waves / Q000502:physiology*; D003627:Data Interpretation, Statistical; D004569:Electroencephalography; D006801:Humans; D011237:Predictive Value of Tests; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology*; D013997:Time Factors
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