Determination of autoregressive model orders for seizure detection

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AYDIN S.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.18, no.1, pp.23-30, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 18 Issue: 1
  • Publication Date: 2010
  • Doi Number: 10.3906/elk-0906-83
  • Journal Name: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.23-30
  • Keywords: EEG, seizure, A R model, stepwise least square algorithm, EEG, EIGENMODES, PARAMETERS
  • Ondokuz Mayıs University Affiliated: No

Abstract

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.