The ultimate goal of epilepsy therapies is to provide seizure control for all patients while eliminating side effects. Improved specificity of intervention through on-demand approaches may overcome many of the limitations of current intervention strategies. This article reviews the progress in seizure prediction and detection, potential new therapies to provide improved specificity, and devices to achieve these ends. Specifically, we discuss (1) potential signal modalities and algorithms for seizure detection and prediction, (2) closed-loop intervention approaches, and (3) hardware for implementing these algorithms and interventions. Seizure prediction and therapies maximize efficacy, whereas minimizing side effects through improved specificity may represent the future of epilepsy treatments.
*Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN;
†Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN;
‡Department of Anatomy & Neurobiology, University of California, Irvine, CA;
§INSERM U1099 LTSI, University of Rennes 1, Rennes, France; and
‖Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN.
Address correspondence and reprint requests to Theoden I. Netoff, PhD, Department of Biomedical Engineering, University of Minnesota, 312 Church St SE, 7-105 NHH, Minneapolis, MN 55455; e-mail: firstname.lastname@example.org.
This study was supported by the US National Institutes of Health (NIH) grant K99NS087110 (E.K.-M.), Citizens United for Research in Epilepsy (CURE) Taking Flight Award (E.K.-M.), US NIH grant NS074432 (I.S.), NSF CAREER award from GARDE (T.I.N.), Epilepsy Foundation grant (T.I.N.), NIH predoctoral training grant (T32) (V.N.), 3M Science and Technology Fellowship (V.N.), and Cyberonics, Inc (S.L. and P.I.).