Determination of autoregressive model orders for seizure detection

Creative Commons License

Aydin S.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.18, pp.23-30, 2010 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 18
  • Publication Date: 2010
  • Doi Number: 10.3906/elk-0906-83
  • Page Numbers: pp.23-30
  • Keywords: EEG, seizure, A R model, stepwise least square algorithm, EEG, EIGENMODES, PARAMETERS


In the present study, a step-wise least square estimation algorithm (SLSA), unplemented in a Matlab package called as A Rfit, has been newly applied to clinical data for estimation of the accurate Auto-Regressive (AR) model orders of both normal and ietal EEG series where the power spectral density (PSD) estimations are provided by the Burg Method. The ARfit module is found to be useful in comparison to a large variety of traditional methods such as Forward Prediction Error (FPE), Akaike's Information Criteria (AIC), Minimum Description Lenght (MDL), and Criterion of Autoregression Transfer function (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.

Key Words: EEG, seizure, AR model, stepwise least square algorithm