Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Application of Periodogram and AR Spectral Analysis to EEG Signals
Journal of Medical Systems
Computers and Electrical Engineering
Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection
Computers in Biology and Medicine
Epileptic EEG detection using the linear prediction error energy
Expert Systems with Applications: An International Journal
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Electroencephalography is an essential clinical tool for the evaluation and treatment of neurophysiologic disorders related to epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important element in the diagnosis of epilepsy. In this study, EEG signals recorded from 30 subjects were processed using autoregressive (AR) method and EEG power spectra were obtained. The parameters of autoregressive method were estimated by different methods such as Yule-Walker, covariance, modified covariance, Burg, least squares, and maximum likelihood estimation (MLE). EEG spectra were then used to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complexes in patients with absence seizures. The variations in the shape of the EEG power spectra were examined in order to obtain medical information. These power spectra were then used to compare the applied methods in terms of their frequency resolution and determination of epileptic seizure. The Cramer-Rao bounds (CRB) were derived for the estimated AR parameters of the EEG signals and the performance evaluation of the estimation methods was performed using the CRB values. Finally, the optimal AR spectral estimation method for the EEG signals was selected according to the computed CRB values. According to the computed CRB values, the performance characteristics of the MLE AR method was found extremely valuable in EEG signal analysis.