title: Seizure Prediction: Science Fiction or Soon to Become Reality?
abstract: 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 micro-electrode 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.
authors.affiliation: Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia, 3065. deanrf@unimelb.edu.au.|Department of Statistics, Columbia University, New York, NY, 10027, USA. deanrf@unimelb.edu.au.
authors.forename: Dean R
authors.initials: DR
authors.lastname: Freestone
authors.affiliation: Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia, 3065.|Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, Australia, 3000.
authors.forename: Philippa J
authors.initials: PJ
authors.lastname: Karoly
authors.affiliation: Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia, 3065.
authors.forename: Andre D H
authors.initials: AD
authors.lastname: Peterson
authors.affiliation: Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, Australia, 3000.
authors.forename: Levin
authors.initials: L
authors.lastname: Kuhlmann
authors.affiliation: Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, Australia, 3000.
authors.forename: Alan
authors.initials: A
authors.lastname: Lai
authors.affiliation: Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, Australia, 3000.
authors.forename: Farhad
authors.initials: F
authors.lastname: Goodarzy
authors.affiliation: Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia, 3065. markcook@unimelb.edu.au.
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Cook
mesh_terms: D000818:Animals; D004566:Electrodes; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D009488:Neurosciences / Q000379:methods; D011788:Quality of Life; D012640:Seizures / Q000503:physiopathology*
keywords: Active EEG; Ambulatory EEG; Computational neuroscience; Electrophysiology; Epileptic seizure prediction; Micro-electrode; Review