A NOVEL FRAMEWORK FOR CARDIAC ARRHYTHMIA CLASSIFICATION BASED ON SUBSPACE PROJECTION AND DECISION TREE

Year 2020, Volume: 21 Issue: 2, 350 - 365, 15.06.2020

Semih Ergin , Selcan Kaplan Berkaya , Alper Kürşat Uysal , Efnan Şora Günal , Serkan Gunal , Mehmet Bilginer Gülmezoğlu

https://doi.org/10.18038/estubtda.630634

Abstract

Cardiac arrhythmia basically refers to
abnormal activity of heart. Correct classification of cardiac arrhythmia is
therefore crucial for appropriate treatment of heart diseases. In this paper, a
novel approach is proposed for cardiac arrhythmia classification. Initially,
the feature vectors extracted from raw electrocardiogram (ECG) signals are
projected into a particular subspace obtained via the Common Vector Approach,
which is an effective subspace method. The projected vectors are then fed into
two distinct decision-tree-based classifiers—namely, C4.5 and random forest.
The results obtained from the proposed approach are compared with those
obtained with the original feature vectors using the same classifiers. For this
purpose, the well-known MIT-BIH arrhythmia database was utilized. Six different
sets of features based on QRS, time-domain, wavelet transform and power
spectral density are derived from ECG signals in this database. The feature
sets are then used in the classification of five main beat types including
non-ectopic, ventricular ectopic, supraventricular ectopic, fusion and unknown.
The experimental results reveal that the recognition performances achieved by
most of the projected features are explicitly higher than those obtained with
the original ones. In addition, the classification accuracy of the proposed
approach climbs to 100% for the test set.

Keywords

Cardiac arrhythmia classification, electrocardiography, feature selection, subspace projection, decision tree

Supporting Institution

Eskiehir Osmangazi University

Project Number

201215037

Thanks

This work was supported by Eskisehir Osmangazi University, Fund of Scientific Research Projects under grant number 201215037.

References

• [1] Martis, R.J., Acharya, U.R., Min, L.C. ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform. Biomed Signal Proces 2013; 8: 437-448. doi:10.1016/j.bspc.2013.01.005.
• [2] Martis, R.J., Acharya, U.R., Mandana, K.M., Ray, A.K., Chacraborty, C. Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Syst Appl 2012; 39: 11792-11800. doi:10.1016/j.eswa.2012.04.072.
• [3] Haseena, H.H., Mathew, A.T., Paul, J.K. Fuzzy clustered probabilistic and multi layered feed forward neural networks for electrocardiogram arrhythmia classification. J Med Syst 2011; 35: 179-188. doi:10.1007/s10916-009-9355-9.
• [4] Llamedo, M., Martinez, J.P. An automatic patient-adapted ECG heartbeat classifier allowing expert assistance. IEEE T Bio-Med Eng 2012; 59: 2312-2320. doi:10.1109/Tbme.2012.2202662.
• [5] Ince, T., Kiranyaz, S., Gabbouj, M. A generic and robust system for automated patient-specific classification of ECG signals. IEEE T Bio-Med Eng 2009; 56: 1415-1426. doi:10.1109/Tbme.2009.2013934.
• [6] Mondéjar-Guerra, V., Novo, J., Rouco, J., Penedo, M. G., Ortega, M. Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomed Signal Proces 2019; 47: 41-48. doi: 10.1016/j.bspc.2018.08.007.
• [7] Li, T., Zhou, M. ECG classification using wavelet packet entropy and random forests. Entropy 2016; 18: 285. doi: 10.3390/e18080285.
• [8] Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A., San Tan, R. A deep convolutional neural network model to classify heartbeats. Comput Biol Med 2017; 89: 389-396. doi: 10.1016/j.compbiomed.2017.08.022.
• [9] Kiranyaz, S., Ince, T., Gabbouj, M. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE T Bio-Med Eng 2016; 63: 664-675. doi: 10.1109/TBME.2015.2468589.
• [10] Jiang, W., Kong, S.G. Block-based neural networks for personalized ECG signal classification. IEEE T Neural Networ 2007; 18: 1750-1761. doi:10.1109/Tnn.2007.900239.
• [11] Inan, O.T., Giovangrandi, L., Kovacs, G.T.A. Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE T Bio-Med Eng 2006; 53: 2507-2515. doi:10.1109/TBME.2006.880879.
• [12] de Chazal, P., Reilly, R.B. A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE T Bio-Med Eng 2006; 53: 2535-2543. doi:10.1109/TBME.2006.883802.
• [13] Hu, Y.H., Palreddy, S., Tompkins, W.J. A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE T Bio-Med Eng 1997; 44: 891-900. doi:10.1109/10.623058.
• [14] Elhaj, F.A., Salim, N., Harris, A.R., Swee, T.T., Ahmed, T. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput Meth Prog Bio 2016; 127: 52-63. doi:10.1016/j.cmpb.2015.12.024.
• [15] Khushaba, R.N., Kodagoda, S., Lal, S., Dissanayake, G. Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE T Bio-Med Eng 2011; 58: 121-131. doi:10.1109/Tbme.2010.2077291.
• [16] Ubeyli, E.D. Statistics over features of ECG signals. Expert Syst Appl 2009; 36: 8758-8767. doi:10.1016/j.eswa.2008.11.015.
• [17] Gunal, S., Ergin, S., Gunal, E.S., Uysal, K., U. ECG classification using ensemble of features. In: IEEE 47th Annual Conference on Information Sciences and Systems; 20-22 March 2013; Baltimore, MD, USA: IEEE, 1-5. doi:10.1109/CISS.2013.6624256.
• [18] Mishra, A.K., Raghav, S. Local fractal dimension based ECG arrhythmia classification. Biomed Signal Proces 2010; 5: 114-123. doi:10.1016/j.bspc.2010.01.002.
• [19] Giri, D., Acharya, U.R., Martis, R.J., Sree, S.V., Lim, T., Vi, T.A., Suri, J.S. Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowl-Based Syst 2013; 37: 274-282. doi:10.1016/j.knosys.2012.08.011.
• [20] Wang, J.S., Chiang, W.C., Hsu, Y.L., Yang, Y.T.C. ECG ECG arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing 2013; 116: 38-45. doi:10.1016/j.neucom.2011.10.045.
• [21] Li, H., Liang, H., Miao, C., Cao, L., Feng, X., Tang, C., Li, E. Novel ECG signal classification based on KICA nonlinear feature extraction. Circuits, Systems, and Signal Processing 2016; 35: 1187-1197. doi:10.1007/s00034-015-0108-3.
• [22] Gulmezoglu, M.B., Dzhafarov, V., Barkana, A. The common vector approach and its relation to principal component analysis. IEEE Transactions on Speech and Audio Processing 2001; 9: 655-662. doi:10.1109/89.943343.
• [23] Theodoridis, S. and Koutroumbas, K. Pattern Recognition. 4th ed. Academic Press, 2009.
• [24] Sadic, S., Gulmezoglu, M.B. Common vector approach and its combination with GMM for text-independent speaker recognition. Expert Syst Appl 2011; 38: 11394-11400. doi:10.1016/j.eswa.2011.03.009.
• [25] Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E. PhysioBank, PhysioToolkit, and PhysioNet - Components of a new research resource for complex physiologic signals. Circulation 2000; 101: E215-E220.
• [26] Ergin, S., Uysal, A.K., Gunal, E.S., Gunal, S., Gulmezoglu, M.B. ECG based biometric authentication using ensemble of features. In: IEEE 9th Iberian Conference on Information Systems and Technologies; 18-21 June 2014; Barcelona, Spain. doi:10.1109/CISTI.2014.6877089.
• [27] Pan, J., Tompkins, W.J. A real-time QRS detection algorithm. IEEE T Bio-Med Eng 1985; 32: 230-236. doi: 10.1109/Tbme.1985.325532.
• [28] Valentin, J.P. Reducing QT liability and proarrhythmic risk in drug discovery and development. Brit J Pharmacol 2010; 159: 5-11. doi:10.1111/j.1476-5381.2009.00547.x.
• [29] Biel, L., Pettersson, O., Philipson, L., Wide, P. ECG analysis: A new approach in human identification. IEEE T Instrum Meas 2001; 50: 808-812. doi: 10.1109/19.930458.
• [30] Stravroulakis, P. Stamp, M. Handbook of Information and Communication Security. Springer, 2010.
• [31] Mallat, S. A Wavelet Tour of Signal Processing. Academic Press, 2001.
• [32] Coifman, R.R., Wickerhauser, M.V. Entropy-based algorithms for best basis selection. IEEE T Inform Theory 1992; 38: 713-718. doi: 10.1109/18.119732.
• [33] Welch, P.D. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics 1967; 15: 70-73. doi:10.1109/TAU.1967.1161901.
• [34] Mandelbrot, B.B. The Fractal Geometry of Nature. W. H. Freeman and Company, 1983.
• [35] Spasic, S. Spectral and fractal analysis of biosignals and coloured noise. In: IEEE 5th International Symposium on Intelligent Systems and Informatics; 24-25 August 2007; Subotica, Serbia. IEEE, 147-149. doi:10.1109/SISY.2007.4342641.
• [36] Gulmezoglu, M.B., Keskin, M., Dzhafarov, V., Barkana, A. A novel approach to isolated word recognition. IEEE Transactions on Speech and Audio Processing 1999; 7: 620-628. doi:10.1109/89.799687.
• [37] Gulmezoglu, M.B., Ergin, S. An approach for bearing fault detection in electrical motors. European Transactions on Electrical Power 2007; 17: 628-641. doi:10.1002/etep.161.
• [38] Landgrebe, D. Hyperspectral image data analysis. IEEE Signal Processing Magazine 2002; 19: 17-28. doi:10.1109/79.974718.
• [39] Oja, E. Subspace Methods of Pattern Recognition. John Wiley and Sons, 1983.
• [40] Swets, D.L., Weng, J. Using discriminant eigenfeatures for image retrieval. IEEE T Pattern Anal 1996; 18: 831-836. doi:10.1109/34.531802.
• [41] Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A. Discriminative common vectors for face recognition. IEEE T Pattern Anal 2005; 27: 4-13. doi:10.1109/TPAMI.2005.9.
• [42] Yumurtaci, M., Gokmen, G., Arikan, C., Ergin, S., Kilic, O. Classification of short circuit faults in high voltage energy transmission line using energy of instantaneous active power components based common vector approach. Turk J Electr Eng Co 2016; 24: 1901-1915. doi:10.3906/elk-1312-131.
• [43] Gunal, S., Ergin, S., Gulmezoglu, M.B., Gerek, O.N. On feature extraction for spam e-mail detection.Lecture Notes on Computer Science 2006; 4105: 635-642. doi:10.1007/11848035_84.
• [44] Gunal, S., Edizkan, R. Subspace based feature selection for pattern recognition. Inform Sciences 2008; 178: 3716-3726. doi:10.1016/j.ins.2008.06.001.
• [45] Gunal, S. Hybrid feature selection for text classification. Turk J Electr Eng Co 2012; 20: 1296-1311. doi:10.3906/elk-1101-1064.
• [46] Quinlan, J.R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
• [47] Ho, T.K. Random decision forests. In: IEEE Proceedings of the Third International Conference on Document Analysis and Recognition; 14-16 August 1995; Montreal, Quebec, Canada. IEEE, 278-282. doi:10.1109/ICDAR.1995.598994.
• [48] ANSI/AAMI EC57: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms (AAMI Recommended Practice/American National Standard), Order Code: EC57-293, 1998.
• [49] AAMI Recommendation, Recommended Practice for Testing and Reporting Performance Results of Ventricular Arrhythmia Detection Algorithms, Association for the Advancement of Medical Instrumentation, 1987.
• [50] Afkhami, R.G., Azarnia, G., Tinati, M.A. Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals. Pattern Recogn Lett 2016; 70: 45-51. doi:10.1016/j.patrec.2015.11.018.
• [51] Shadmand, S., Mashoufi, B. A new personalized ECG signal classification algorithm using Block-based Neural Network and Particle Swarm Optimization. Biomed Signal Proces 2016; 25: 12-23. doi:10.1016/j.bspc.2015.10.008.
• [52] Queiroz, V., Luz, E., Moreira, G., Guarda, A., Menotti, D. Automatic cardiac arrhythmia detection and classification using vectorcardiograms and complex networks, In: IEEE 37th Annual International Conference of the Engineering in Medicine and Biology Society; 25-29 August 2015; Milan, Italy. IEEE, 5203-5206. doi:10.1109/EMBC.2015.7319564.
• [53] Llamedo, M., Martinez, J.P. Heartbeat classification using feature selection driven by database generalization criteria. IEEE T Bio-Med Eng 2011; 58: 616-625. doi:10.1109/Tbme.2010.2068048.
• [54] Castro, D., Félix, P., Presedo, J. A method for context-based adaptive QRS clustering in real time. IEEE J Biomed Health 2015; 19: 1660 - 1671. doi:10.1109/JBHI.2014.2361659.
• [55] Escalona-Morán, M. A., Soriano, M. C., Fischer, I., Mirasso, C. R. Electrocardiogram classification using reservoir computing with logistic regression. IEEE J Biomed Health 2015; 19: 892-898. doi: 10.1109/JBHI.2014.2332001.
• [56] Yang, W., Si, Y., Wang, D., Guo, B. Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine. Comput Biol Med 2018; 101: 22-32. doi: 10.1016/j.compbiomed.2018.08.003.