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Non-parametric early seizure detection in an animal model of temporal lobe epilepsy

Lookup NU author(s): Dr Jennifer Simonotto

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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. © 2008 IOP Publishing Ltd.

Publication metadata

Author(s): Talathi SS, Hwang D-U, Spano ML, Simonotto J, Furman MD, Myers SM, Winters JT, Ditto WL, Carney PR

Publication type: Article

Publication status: Published

Journal: Journal of Neural Engineering

Year: 2008

Volume: 5

Issue: 1

Pages: 85-98

ISSN (print): 1741-2560

ISSN (electronic): 1741-2552

Publisher: Institute of Physics Publishing Ltd.

URL: http://dx.doi.org/10.1088/1741-2560/5/1/009

DOI: 10.1088/1741-2560/5/1/009

PubMed id: 18310814

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