Abstract
Proposed methods for feature extraction and selection stages of biomedical pattern recognition system are presented. Time-Frequency signal analysis based on adaptive wavelet transform and Principle Component Algorithm (PCA) algorithm is used for extracting and selecting from original data the input features that are most predictive for a given outcome. From the discrete fast wavelet transform coefficients optimal feature set based on energy and entropy of wavelet components is created. Then PCA is used to shrink this feature group by creating the most representative parameter subset for given problem, which is the input for last neural classifier stage. System was positively verified on the set of clinically classified ECG signals for control and atrial fibrillation (AF) disease patients taken from MITBIH data base. The measures of specificity and sensitivity computed for the set of 20 AF and 20 patients from control group divided into learning and verifying subsets were used to evaluate presented pattern recognition structure. Different types of wavelet basic function for feature extraction stage as well as supervised (Multilayer Perceptron) and unsupervised (Self Organizating Maps) neural network classification units were tested to find the best system structure.
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. (1973) Pattern classification and scene analysis. John Wiley & Sons, New York.
Rakotomamonjy A (2003) Variable Selection Using SVM-based criteria. Journal of Machine Learning Research 3:1357–1370.
Kostka P.S., Tkacz E.J. (2006), Hybrid Feature Vector Creation for Atrial Fibrillation Detection Improvement, Proc. of World Congress of Medical Physics and Biomedical Engineering, Seoul.
Mallat S. (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans on Pattern An 7(11):674–693
Akay M. Time-Frequency and wavelet analysis. IEEE EMB Magazine 14(2)
Thakor N.V., Sherman D.L. (1994) Biomedical problems in timefrequency-scale analysis – new challenges, Proceedings of the IEEESP pp. 536–539, 1994.
Kostka P.S., Tkacz E.J. (2005), Feature extraction optimization in neural classifier of heart rate variability signals, Proceedings of the 4th International Conference on Computer Recognition Systems CORES 2005, Advances in Soft Computing, Springer-Verlag, pp.585–594.
Karhunen, Joutsensalo (1995), Generalizations of principle component analysis, optimization problems, and neural networks. Neural Networks 8(4):549–562.
Friedman W.J., Tukey J.W. (1974) A proj. pursuit alg. for exploratory data analysis, IEEE Trans on Com 23:881–889.
Fukunaga K. (1990) Introduction to statistical pattern recognition. 2nd edition. Academic Press, San Diego, CA.
Bishop C.M, (1996) Neural Networks for Pattern Recognition. Oxford University Press, New York
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kostka, P., Tkacz, E. (2007). Feature extraction and selection algorithms in biomedical data classifiers based on time-frequency and principle component analysis.. In: Jarm, T., Kramar, P., Zupanic, A. (eds) 11th Mediterranean Conference on Medical and Biomedical Engineering and Computing 2007. IFMBE Proceedings, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73044-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-73044-6_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73043-9
Online ISBN: 978-3-540-73044-6
eBook Packages: EngineeringEngineering (R0)