Determination of autoregressive model orders for seizure detection

Authors : Serap AYDIN

Pages : 23-30

View : 11 | Download : 11

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :In the present study, a step-wise least square estimation algorithm insert ignore into journalissuearticles values(SLSA);, implemented in a Matlab package called as ARfit, has been newly applied to clinical data for estimation of the accurate Auto-Regressive insert ignore into journalissuearticles values(AR); model orders of both normal and ictal EEG series where the power spectral density insert ignore into journalissuearticles values(PSD); estimations are provided by the Burg Method. The ARfit module is found to be usefull in comparison to a large variety of traditional methods such as Forward Prediction Error insert ignore into journalissuearticles values(FPE);, Akaike`s Information Criteria insert ignore into journalissuearticles values(AIC);, Minimum Description Lenght insert ignore into journalissuearticles values(MDL);, and Criterion of Autoregressive Transfer function insert ignore into journalissuearticles values(CAT); for EEG discrimination. According to tests, the FPE, AIC and CAT give the identical orders for both normal and epileptic series whereas the MDL produces lower orders. Considering the resulting PSD estimations, it can be said that the most descriptive orders are provided by the SLSA. In conclusion, the SLSA can mark the seizure, since the estimated AR model orders meet the EEG complexity/regularity such that the low orders indicate an increase of EEG regularity in seizure. Then, the SLSA is proposed to select the accurate AR orders of long EEG series in diagnose for many possible future applications. The SLSA implemented by ARfit module is found to be superior to traditional methods since it is not heuristic and it is less computational complex. In addition, the more reasonable orders can be provided by the SLSA.
Keywords : Key words EEG, seizure, AR model, stepwise least square algorithm