Application of Classical and Model-Based Spectral Methods to Describe the State of Alertness in EEG

Authors:
Abdulhamit Subasi
Affiliations:
Department of Electrical and Electronics Engineering, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey 46601
Venue:
Journal of Medical Systems
Year:
2005

Citing 4
Cited 2

Digital signal processing (3rd ed.): principles, algorithms, and applications

Digital signal processing (3rd ed.): principles, algorithms, and applications
Neural Networks with Periodogram and Autoregressive Spectral Analysis Methods in Detection of Epileptic Seizure

Journal of Medical Systems
Neural network analysis of ophthalmic arterial doppler signals with Uveitis disease

Neural Computing and Applications
On-line spectral estimation of nonstationary time series based onAR model parameter estimation and order selection with a forgettingfactor

IEEE Transactions on Signal Processing

Channel selection and feature projection for cognitive load estimation using ambulatory EEG

Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network

Journal of Medical Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. In this study, EEG signals recorded from 30 subjects were processed by PC-computer using classical and model-based methods. The classical method (fast Fourier transform) and three model-based methods (Burg autoregresse, moving average, least-squares modified Yule---Walker autoregressive moving average methods) were selected for processing EEG signals to discriminate the alertness level of subject. Power spectra of EEG signals were obtained by using these spectrum analysis techniques. These EEG spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of vigilance state of subject. It is found that, FFT and MA methods have low spectral resolution, these two methods are not appropriate for the analysis of the a wake---sleep correlation. Burg AR and least-squares modified Yule---Walker ARMA methods' performance characteristics have been found extremely valuable for the determination of vigilance state of a healthy subject, because of their clear spectra.