IOP PUBLISHING JOURNAL OF NEURAL ENGINEERING J. Neural Eng. 5 (2008) 85–98 doi:10.1088/1741-2560/5/1/009 Non-parametric early seizure detection in an animal model of temporal lobe epilepsy Sachin S Talathi 1,2,3 , Dong-Uk Hwang 1 , Mark L Spano 4 , Jennifer Simonotto 5 , Michael D Furman 6 , Stephen M Myers 1,2,3 , Jason T Winters 1,2,3 , William L Ditto 1,2,3 and Paul R Carney 1,2,3,7,8,9 1 J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, FL 32611, USA 2 Wilder Epilepsy Research Center, University of Florida, FL 32611, USA 3 McKnight Brain Institute, University of Florida, FL 32611, USA 4 NSWC, Carderock Laboratory, W Bethesda, MD 20817, USA 5 School of Computing Science and Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK 6 Signal Processing and Communications Laboratory, Department of Engineering, University of Cambridge, USA 7 Department of Pediatrics, University of Florida, FL 32611, USA 8 Department of Neuroscience, University of Florida, FL 32611, USA 9 Department of Neurology, University of Florida, FL 32611, USA E-mail: stalathi@bme.ufl.edu Received 28 November 2007 Accepted for publication 5 February 2008 Published 27 February 2008 Online at stacks.iop.org/JNE/5/85 Abstract The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure. For the EEG data recorded with microwire electrode array at a sampling rate of 12 kHz, the wavelet scale measure exhibited better overall performance in terms of its ability to detect a seizure with high optimality index value and high statistics in terms of sensitivity and specificity. (Some figures in this article are in colour only in the electronic version) 1. Introduction Epilepsy is an episodic brain dysfunction characterized by recurrent, seemingly unpredictable, spontaneous seizures [22]. The occurrence of seizures in patients without any forewarning is the most debilitating aspect of the disease. A great deal of scientific research has therefore focused on developing methods for anticipating and/or detecting seizures early enough [14] to facilitate timely therapeutic intervention, with a consequent improvement in the quality of life of the epileptic patient. The goal of seizure prediction or detection is the development of a system that not only forewarns of a seizure but also employ measures to prevent it. Seizure anticipation algorithms can be broadly classified into two categories depending on the time horizon of the forecast: (a) seizure prediction algorithms that aim to detect the preictal state in the EEG minutes to hours in advance of an impending seizure [6, 7, 11–13, 19] and (b) early seizure detection algorithms that use EEG data to identify seizure onset, typically a few seconds in advance of the onset of 1741-2560/08/010085+14$30.00 © 2008 IOP Publishing Ltd Printed in the UK 85