EEG Sleep Staging Using Vectorial Autoregressive Models
Sleep studies require the use of several channels of EEG. The analysis of vector EEG, exhibits significant advantages over scalar analysis. Novel algorithms for segmentation, classification and compression of vector EEG are described. The statistics of the suggested measures for segmentation and classification are discussed. The algorithms were evaluated on four patients, yielding mean correct sleep staging of about 85%.
KeywordsSleep Stage Distortion Measure Inverse Filter Neighbor Rule Codebook Size
Unable to display preview. Download preview PDF.
- Gersch, W. Yonemoto, J., and Naitoh, P., 1977b, Automatic classification of multivariate EEGs using an amount of information and the eigenvalues of parametric time series model features, Computers in Biomed. Res., 17: 352–361.Google Scholar
- Haykin, S. and Kesler. S., 1979. Prediction error filtering and maximum entropy spectral estimation, in: Haykin. S., (ed.), Topics in Applied Physics. 34: 9–70, Springer Verlag, Berlin.Google Scholar
- Jenkins. G. M. and Watts, D. G.. 1986, Spectral Analysis and its Applications, Holden Day, San Francisco, Ca.Google Scholar
- Kay. S. M.. 1988, Modem Spectral Estimation: Theory and Applications, Prentice Hall. Engelwood Cliffs. N.J.Google Scholar
- Linde,Y.. Buzo. A., and Gray. R. M.. 1980. An algorithm for vector quantizer design. IEEE Trans. Comm., COM-28: 84–95.Google Scholar
- Marple, S. L., 1987, Digital Spectral Analysis with Applications. Prentice Hall, Engelwood Cliffs, N.J.Google Scholar
- Priestly, M. B., Spectral Analysis and Time Series, 1981, Academic Press. N.Y.Google Scholar
- Sanderson, A. C. Segen, J., and Richey, E., 1980, Hierarchical modeling of EEG signals, IEEE Trans. Patt. Anal. Mach. Intell., PAMI-2: 405–414.Google Scholar