Journal of Medical Systems

, Volume 30, Issue 6, pp 413–419 | Cite as

Comparison of AR and Welch Methods in Epileptic Seizure Detection

Original Paper

Abstract

Brain is one of the most critical organs of the body. Synchronous neuronal discharges generate rhythmic potential fluctuations, which can be recorded from the scalp through electroencephalography. The electroencephalogram (EEG) can be roughly defined as the mean electrical activity measured at different sites of the head. EEG patterns correlated with normal functions and diseases of the central nervous system. In this study, EEG signals were analyzed by using autoregressive (parametric) and Welch (non-parametric) spectral estimation methods. The parameters of autoregressive (AR) method were estimated by using Yule–Walker, covariance and modified covariance methods. EEG spectra were then used to compare the applied estimation methods in terms of their frequency resolution and the effects in determination of spectral components. The variations in the shape of the EEG power spectra were examined in order to epileptic seizures detection. Performance of the proposed methods was evaluated by means of power spectral densities (PSDs). Graphical results comparing the performance of the proposed methods with that of Welch technique were given. The results demonstrate consistently superior performance of the covariance methods over Yule–Walker AR and Welch methods.

Keywords

EEG Epileptic seizure Spectral analysis Yule–Walker AR Covariance Modified-covariance Welch method 

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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Department of Computer EngineeringYasar UniversityİzmirTurkey
  2. 2.Department of Electrical and Electric EngineeringKahramanmaraş Sütçü İmam UniversityKahramanmaraşTurkey

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