Abstract
In the paper classification method of compressed ECG signal was presented. Classification of single heartbeats was performed by neural networks and support vector machine. Parameterization of ECG signal was realized by principal component analysis (PCA). For every heartbeat only two descriptors have been used. The results of real Holter signal were presented in tables and as plots in planespherical coordinates. The efficiency of classification is near to 99%.
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.
Similar content being viewed by others
References
Bausá Lamazares, D.: Morphological Classification of ECG Holter Recording by Principal Component Analysis (PCA) and Support Vector Machine (SVM), Final Project (SOCRATES/ERASMUS), Warsaw Institute of Technology (2003)
Bishop, C.M.: Neural networks for pattern recognition. Clarendon Press, Oxford
Cover, T.M.: Geometrical and Statistical Properties of Systems of Linear Inequalities with Application in Pattern Recognition. IEEE Transaction on Electronic Computers, 326–334 (June 1965)
Dubois, R.: Application des nouvelles méthodes d’apprentissage à la détectionprécoce d’anomalies en électrocardiographie, Ph.D. thesis, Université Paris 6 (2003)
Jankowski, S., Tijink, J., Vumbaca, G., Balsi, M., Karpiński, G.: Morphological analysis of ECG Holter recordings by support vector machines. In: Colosimo, A., Giuliani, A., Sirabella, P. (eds.) ISMDA 2002. LNCS, vol. 2526, pp. 134–143. Springer, Heidelberg (2002)
Jankowski, S., Oręziak, A., Skorupski, A., Kowalski, H., Szymański, Z., Piątkowska-Janko, E.: Computer-aided Morphological Analysis of Holter ECG Recordings Based on Support Vector Learning System. In: Proc. IEEE International Conference Computers in Cardiology 2003, Tessaloniki, pp. 597–600 (2003)
Jaworski, R.: Factoring of ECG Cycles by Computing Eigenvectors and Eigenvalues of Covariance Matrixes (in Polish), M. Sc. Thesis, Warsaw Institute of Technology (2004)
Medilog Oxford Management Systems, http://www.scanmed.co.uk/oxford.htm
Wagner, G.S.: Marriot’s Practical Electrocardiography. Williams & Wilkins (1994)
Vapnik, N.V.: Statistical Learning Theory. John Wiley & Sons Inc., New York (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jankowski, S., Dusza, J.J., Wierzbowski, M., Oręziak, A. (2004). PCA Representation of ECG Signal as a Useful Tool for Detection of Premature Ventricular Beats in 3-Channel Holter Recording by Neural Network and Support Vector Machine Classifier. In: Barreiro, J.M., Martín-Sánchez, F., Maojo, V., Sanz, F. (eds) Biological and Medical Data Analysis. ISBMDA 2004. Lecture Notes in Computer Science, vol 3337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30547-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-30547-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23964-2
Online ISBN: 978-3-540-30547-7
eBook Packages: Springer Book Archive