Abstract
In this chapter, we study an ECG signal processing and beat classification problem in particular, considering a technique coming with high interpretation capabilities, namely a family of hyperbox classifiers. Different techniques based on a combination of fuzzy clustering and genetic algorithms have been applied to support learning processes in the specific pattern recognition task. Hyperbox classifiers have been investigated for the detection and classification of different types of heartbeats in electrocardiograms (ECGs), which are of major importance in the diagnosis of cardiac dysfunctions. In particular, the learning capacity and the classification ability for normal beats (N) and premature ventricular contractions (PVC) have been tested, with focus on the interpretability aspect. The MIT-BIH arrhythmia database has been used for testing and validating of the proposed method. A total of 26 morphology features have been extracted from ECG and reconstructed VCG signals. Three learning procedures have been developed and analyzed. We have elaborated on several ways of combining fuzzy clustering and genetic algorithm in identifying the optimal hyperboxes and forming a family of hyperellipsoids. The results showed that even a limited number of hyperboxes can increase the geometrical interpretability without a significant reduction of the accuracy of such classifier.
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
References
AAMI.: Testing and reporting performance results of cardiac rhythm and ST-segment measurement algorithms. Ansi/AAMI EC57:1998/(R)2008. Association for the Advancement of Medical Instrumentation (2008)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function. Plenum, New York (1981)
Bortolan, G. and Pedrycz, W.: Fuzzy descriptive models: an interactive framework of information granulation. IEEE Trans. Fuzzy Sets. 10, 743–755 (2002a)
Bortolan, G., Pedrycz, W.: An interactive framework for an analysis of ECG signals. Artif. Intell. Med. 24, 109–132 (2002b)
Bortolan, G., Pedrycz, W.: Hyperbox classifier for arrhythmia classification. Kybernetes. 36, 531–547 (2007)
Bortolan, G., Willems, J.L.: Diagnostic ECG classification based on neural networks. J. Electrocardiol. 26, 75–79 (1994)
Bortolan, G., Brohet, C. Fusaro, S.: Possibilities of using neural networks for diagnostic ECG classification. J. Electrocardiol. 29, 10–16 (1996)
Bortolan, G., Jekova, I., Christov, I.: Comparison of four methods for premature Ventricular Contraction and normal beat clustering. IEEE Comp. Cardiol. 32, 921–924 (2005)
Bortolan, G., Christov, I., Pedrycz, W.: Hyperbox classifiers for ECG beat analysis. IEEE Comp. Cardiol. 34, 145–148 (2007)
Christov, I., Bortolan, G.: Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks. Physiological Measurement, 24, 1281–1290 (2004)
Christov, I., Jekova, I., Bortolan, G.: Premature ventricular contraction classification by the Kth nearest-neighbours rule. Physiol. Meas. 26, 123–130 (2005)
Christov, I., Gómez-Herrero, G., Krasteva, V., Jekova, I., Gotchev, A., Egiazarian, K.: Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Med. Eng. Phys. 28, 876–887 (2006)
Conn, A.R., Gould, N.I.M., Toint, P.L.: A globally convergent augmented Lagrangian pattern search algorithm for optimization with general constraints and simple bounds. SIAM J. Numer. Anal. 28, 545–572 (1991)
Daskalov, I.K., Christov, I.: Electrocardiogram signal preprocessing for automatic detection of QRS boundaries. Med. Eng. Phys. 21, 37–44 (1999)
Daskalov, I., Dotsinsky, I., Christov, I.: Developments in ECG acquisition, preprocessing, parameter measurement and recording. IEEE Eng. Med. Biol. 17, 50–58 (1998)
Gabrys, B., Bargiela, A.: General fuzzy min-max neural network for clustering and classification. IEEE Trans. Neural Netw. 11, 769–783 (2001)
Gómez-Herrero, G., Gotchev, A., Christov, I., Egiazarian, K.: Feature extraction for heartbeat classification using independent component analysis and matching pursuits. In: International Conference on Acoustics, Speech and Signal Processing, ICASSP2005, pp. 725–728, Philadelphia (2005)
Gómez-Herrero, G., Jekova, I., Krasteva, V., Christov, I., Gotchev, A., Egiazarian, K.: Relative estimation of the Karhunen-Loève transform basis functions for detection of ventricular ectopic beats. IEEE Comp. Cardiol. 33, 569–572 (2006)
Jaklic, A., Leonardis, A., Solina, F.: Segmentation and Recovery of Superquadrics. Kluwer Academic, London (2000)
Jekova, I., Bortolan, G., Christov, I.: Pattern recognition and optimal parameter selection in premature ventricular contraction classification. IEEE Comp. Cardiol. 31, 357–360 (2004)
Jekova, I., Bortolan, G., Christov, I.: Assessment and comparison of different classification methods for heart beat clustering. Med. Eng. Phys. 30, 248–257 (2008)
Mark, R., Moody, G.: MIT-BIH Arrhythmia Data Base Directory. Massachusetts Institute of Technology, Cambridge (1988)
Silipo, R., Bortolan, G., Marchesi, C.: Design of hybrid architectures based on neural classifier and RBF pre-processing for ECG analysis. Int. J. Approx Reason. 21, 177–196 (1999)
Simpson, P.K.: Fuzzy min-max neural networks: 1. Classification. IEEE Trans. Neural Netw. 3, 776–586 (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London Limited
About this chapter
Cite this chapter
Bortolan, G., Christov, I., Pedrycz, W. (2012). Hyperellipsoidal Classifier for Beat Classification in ECG Signals. In: Gacek, A., Pedrycz, W. (eds) ECG Signal Processing, Classification and Interpretation. Springer, London. https://doi.org/10.1007/978-0-85729-868-3_10
Download citation
DOI: https://doi.org/10.1007/978-0-85729-868-3_10
Published:
Publisher Name: Springer, London
Print ISBN: 978-0-85729-867-6
Online ISBN: 978-0-85729-868-3
eBook Packages: EngineeringEngineering (R0)