title: Improving early seizure detection.
abstract: Over the last decade, the search for a method able to reliably predict seizures hours in advance has been largely replaced by the more realistic goal of very early detection of seizure onset, which would allow therapeutic or warning devices to be triggered prior to the onset of disabling clinical symptoms. We explore in this article the steps along the pathway from data acquisition to closed-loop applications that can and should be considered to design the most efficient early seizure detection. Microelectrodes, high-frequency oscillations, high sampling rate, high-density arrays, and modern analysis techniques are all elements of the recording and detection process that in combination with modeling studies can provide new insights into the dynamics of seizure onsets. Each of these steps needs to be considered if detection devices that will favorably impact the quality of life of patients are to be implemented. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
authors.affiliation: Epilepsy Research Laboratory, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA. cjouny@jhmi.edu
authors.forename: Christophe C
authors.initials: CC
authors.lastname: Jouny
authors.forename: Piotr J
authors.initials: PJ
authors.lastname: Franaszczuk
authors.forename: Gregory K
authors.initials: GK
authors.lastname: Bergey
mesh_terms: D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology; D003198:Computer Simulation; D042241:Early Diagnosis; D004569:Electroencephalography; D006801:Humans; D008839:Microelectrodes; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology; D013997:Time Factors
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