Non-parametric early seizure detection in an animal model of temporal lobe epilepsy.
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.
Talathi
Sachin S
SS
J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, FL 32611, USA. stalathi@bme.ufl.edu
Hwang
Dong-Uk
DU
Spano
Mark L
ML
Simonotto
Jennifer
J
Furman
Michael D
MD
Myers
Stephen M
SM
Winters
Jason T
JT
Ditto
William L
WL
Carney
Paul R
PR
D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D004567:Electrodes, Implanted; D004569:Electroencephalography; D004833:Epilepsy, Temporal Lobe / Q000175:diagnosis*; D008297:Male; D051381:Rats; D017207:Rats, Sprague-Dawley; D012640:Seizures / Q000175:diagnosis*; D018709:Statistics, Nonparametric