Abstract
In this paper neural classifier system preliminary feature extraction and selection process using time-frequency representation of heart rate variability (HRV) signal is presented. The crucial point of described method is hybrid multi-domain feature set creation, combining different type parameters as well as feature selection based on the measure of class separability property, computed for each extracted feature. Regarding specific properties of non-stationary HRV signal, wavelet transform was chosen as time-frequency representation tool. Presented results are connected both with optimal feature extraction and selection of HRV signals from patient with coronary artery disease as well as classifier performance verification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Duda, R.O. Hart, P.E., Pattern classification and scene analysis, John Wiley and Sons, New York, 1973.
Marciano, F. Bonaduce, D. Petretta, M. Valva, G. Migaux, M.L., Spectral behavior of heart rate variability in acute ischemic episodes,” in Computers in Cardiology. Los Alamitos, CA: IEEE Comput. Soc. Press, 1995, pp.111–114.
Clariá F. Vallverdú M. Caminal P. The effects of coronary occlusion location on the RR signal. IEEE Engineering in Medicine and Biology. July/August 2002.pp.59–64.
Thakor N.V. Sherman D.L. Biomedical problems in time-frequency-scale analysis-new challenges, Proceedings of the IEEE-SP pp. 536–539, 1994.
Saul, J.P. Heart Rate Variability and Sudden Death: What’s the Connection? Amsterdam, The Netherlands: IOS Press, 1995.
Láng, E., Caminal, P., Horváth, G., Jané, R., Vallverdú, M., Slezsák, I., Bayés de Luna, A., Spectral analysis of heart period variance (HPV)-A tool to stratify risk following myocardial infarction,” J. Med. Eng. Techonol., vol. 22, no. 6, pp. 248–256, 1998.
Akay M., Time-Frequency and wavelet analysis, IEEE EMB Magazine 14(2), 1995.
Akay, Y.M., Akay, M. Welkowitz, W., Kostis, J., Noninvasive detection of coronary artery disease, IEEE Engineering in medicine and biology, 11/12, pp. 761–764, 1994.
Tkacz E. Kostka P., An Application of Wavelet Neural Systems for Classification Patients with Coronary Artery Disease Based on HRV Analysis, World Congress on Medical Physics and Biomedical Engineering-Chicago 2000.
Kostka P. Tkacz E., The comparison of the wavelet-neural systems and radial neural networks in classifier structure, 16th International Eurasip Conference Biosignal 2002, pp.46–48, June 2002, Brno, Czech Republic.
Hlawatsch, F., Boudreaux-Bartels, G.F., Linear and quadratic time-frequency signal representations, IEEE Signal Processing Mag., vol. 9, pp. 21–67, July 1992.
Cohen, L., Time-Frequency Analysis, Englewood Cliffs, N.J.: Prentice Hall Signal Processing Series, 1995.
Mallat S. (1989): A theory for multi-resolution signal decomposition: the wavelet representation, IEEE Transaction on Pattern Analysis, 7(11):674–693, 1989.
Karhunen, Joutsensalo, J. Generalizations of principle component analysis, optimization problems, and neural networks, Neural Networks, vol. 8, No. 4, pp. 549–562, 1995.
Friedman, W.J. Tukey, J.W. A projection pursuit algorithm for exploratory data analysis, IEEE Transactions on Computer, Vol. 23, pp. 881–889, 1974.
Fukunaga K. Introduction to statistical pattern recognition. 2nd edition. Academic Press, San Diego, CA,1990
Kullback, S. Leibler, R.A. On information and suffciency, Annals of Mathematics and Statistics, 22, pp. 79–86, 1951.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kostka, P., Tkacz, E. (2005). Feature Extraction Optimization in Neural Classifier of Heart Rate Variability Signals. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_69
Download citation
DOI: https://doi.org/10.1007/3-540-32390-2_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25054-8
Online ISBN: 978-3-540-32390-7
eBook Packages: EngineeringEngineering (R0)