A sparse Laguerre-Volterra autoregressive model for seizure prediction in temporal lobe epilepsy.
A sparse Laguerre-Volterra autoregressive model has been developed as feature extraction from subdural human EEG data for seizure prediction in temporal lobe epilepsy. The use of Laguerre-Volterra kernel can compactly yield an autoregressive model of longer system memory without increasing the number of the coefficients. In 6 sets of seizure, we used a sparse Laguerre-Volterra autoregressive model with 6 coefficients and the decay parameter of 0.2 and obtained the 10-fold cross-validation prediction results of high Matthews correlation coefficients (0.7-1) and low prediction errors (<;15%). These results demonstrate that the sparse Laguerre-Volterra autoregressive model is effective in the feature extraction for seizure prediction. Finally, this sparse Laguerre-Volterra method can be easily adapted to a potentially more powerful nonlinear autoregressive model as the feature extraction rather than linear autoregressive model that we are currently using.
Pen-Ning Yu
Shokofeh A
SA
Naiini
Christi N
CN
Heck
Charles Y
CY
Liu
Dong Song
Theodore W
TW
Berger
D004569:Electroencephalography; D004833:Epilepsy, Temporal Lobe; D006801:Humans; D016014:Linear Models; D017711:Nonlinear Dynamics; D012640:Seizures