Subspace-based methods for the identification of linear time-invariant systems
Automatica (Journal of IFAC) - Special issue on trends in system identification
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
Effects of model errors on waveform estimation using the MUSICalgorithm
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Computers in Biology and Medicine
Selection of optimal AR spectral estimation method for EEG signals using Cramer-Rao bound
Computers in Biology and Medicine
Journal of Medical Systems
Epileptic EEG detection using the linear prediction error energy
Expert Systems with Applications: An International Journal
A novel mobile epilepsy warning system
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Early detection of epileptic seizures based on parameter identification of neural mass model
Computers in Biology and Medicine
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Electroencephalography is an important 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 component in the diagnosis of epilepsy. In this study, we have proposed subspace-based methods to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. 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 the effects in determination of epileptic seizure. Global performance of the proposed methods was evaluated by means of the visual inspection of power spectral densities (PSDs). Graphical results comparing the performance of the proposed methods with that of the autoregressive techniques were given. The results demonstrate consistently superior performance of the proposed methods over the autoregressive ones.