Computer classification of the EEG time series by Kullback information measure
- 1 January 1980
- journal article
- research article
- Published by Taylor & Francis in International Journal of Systems Science
- Vol. 11 (6) , 677-687
- https://doi.org/10.1080/00207728008967046
Abstract
Kullback information plays an important role in measuring the discrepancy between 2 probability density functions. Kullback information was proven equivalent to the spectral error measure, which was developed to estimate the difference between 2 spectral densities. To divide the non-stationary electroencephalogram into stationary subsequences, segmentations of the data during sleep were performed using the spectral error measure. To classify the segmented electroencephalogram into one of the template pattern classes, Kullback information was computed. Kullback information was used as a measure of the distance between the template pattern and the segmented pattern. Human sleep stages determined by the segmentation and the classification of the electroencephalogram, were similar to those determined by a medical doctor.This publication has 7 references indexed in Scilit:
- Segmentation of non-stationary time seriesInternational Journal of Systems Science, 1979
- On the stationarity and normality of the electroencephalographic data during sleep stagesComputer Programs in Biomedicine, 1978
- Feature extraction from the electroencephalogram by adaptive segmentationProceedings of the IEEE, 1977
- Parametric time series models for multivariate EEG analysisComputers and Biomedical Research, 1977
- Linear Discriminant Functions for Stationary Time SeriesJournal of the American Statistical Association, 1974
- Application of a computer-based model for EEG analysisElectroencephalography and Clinical Neurophysiology, 1971
- Toeplitz Forms and Their ApplicationsPublished by University of California Press ,1958