Abstract
This paper presents a diagnostic system for the detection of presence and absence of cardiac Arrhythmia from the Electrocardiogram (ECG) data using the methods of Feature Selection, Feature Extraction and Binary Classification Technique. A hybrid approach of three algorithms namely Rank Correlation, Principal Component Analysis (PCA) and Support Vector Machine (SVM) are applied on the UCI Cardiac Arrhythmia data set for the automatic arrhythmia detection in Arrhythmia Diagnostic System. Spearman Rank Correlation aids the process of dimension reduction and increases the accuracy of the classifier. In this study, SVM has been widely used for classification based diagnosis of diseases. The results obtained after implementation of all the three methods are thus compared as per their accuracy rate in percentages and the performance of the SVM classifier depends on the cost and kernel parameter sigma classification frequency upon the number of attributes selected by Rank Correlation. The experimental method shows that hybrid approach is superior to other approaches.
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
Song, M.H., Lee, J., Cho, S.P., Lee, K.J., Yoo, S.K.: Support Vector Machine Based Arrhythmia Classification Using Reduced Features. International Journal of Control, Automation and Systems 3(4), 571–579 (2005)
Özlem Özcan, N., Gürgen, F.: Fuzzy Support Vector Machines for ECG Arrhythmia Detection. In: International Conference on Pattern Recognition 2010 (2010), doi:10.1109/ICPR.2010.728
Kohli, N., Verma, N.K., Roy, A.: SVM based Methods for Arr-hythmia Classification in ECG. In: Int’l Conf. on Computer & Communication Technology ICCT 2010 (2010), doi:10.1109/ICCCT.2010.5640480
Mahesh, V., Kandaswamy, A., Vimal, C., Sathish, B.: ECG arrhythmia classification based on logistic model tree. Journal of Biomedical Science and Engineering (2009), doi:10.4236/jbise.2009.26058
Acharya, R., Bhat, P.S., Iyengar, S.S., Roo, A., Dua, S.: Classification of heart rate data using artificial neural network and fuzzy equivalence relation. The Journal of the Pattern Recognition Society (2002), doi:10.1016/S0031-3203(02)00063-8
De Chazal, P., Reilly, R.B.: Automatic classification of ECG beats using waveform shape and heart beat interval features. In: IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP 2003), Hong Kong, China, vol. 2, pp. 269–272 (2003), doi:10.1109/ICASSP.2003.1202346
Osowski, S., Linh, T.H.: ECG beat recognition using fuzzy hybrid neural network. IEEE Transaction on Biomedical Engineering 48(11), 1265–1271 (2001), doi:10.1109/10.959322
Guler, I., Ubeyli, E.D.: ECG beat classifier designed by combined neural network model. Pattern Recognition 38, 199–208 (2005), doi:10.1016/j.patcog.2004.06.009
Engin, M.: ECG beat classification using neuro-fuzzy network. Pattern Recognition Letters 25, 1715–1722 (2004), doi:10.1016/j.patrec.2004.06.014
Hsu, H.-H., Hsieh, C.-W.: Feature Selection via Correlation Coefficient Clus tering. Journal of Software 5(12), 1371–1377 (2010), doi:10.4304/jsw.5.12.1371-1377
Liu, D., Cho, S.-Y., Sun, D.-M., Qiu, Z.-D.: A Spearman correlation coeffi-cient ranking for matching-score fusion on speaker recognition. In: TENCON 2010 - 2010 IEEE Region 10 Conference (2010), doi:doi 10.1109/TENCON.2010.5686608
Anuradha, B., Veera Reddy, V.C.: Cardiac Arrhythmia Classification Using Fuzzy Classifier. Journal of Theoretical and Applied Information Technology (2008)
Guvenir, H.A., Demiroz, G., Cekin, A.: A Supervised Machine Learning Algorithm for Arrhythmia Analysis. Computers in Cardiology (1997), doi:10.1109/CIC.1997.647926
Patra, D., Das, M.K., Pradhan, S.: Integration of FCM, PCA and Neural Networks for Classification of ECG Arrhythmias. IAENG International Journal of Computer Science (February 2010), doi:10.1.1.148.4757
Liu, B., Hao, Z., Tsang, E.C.C.: Nesting One-Against-One Algorithm Based on SVM’s for Pattern Classification. IEEE Transactions on Neural Networks 19(12) (December 2008), doi:10.1109/TNN.2008.2003298
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, London (1995) ISBN:0-387-94559-8
Aizeman, M., Braverman, E., Rozonoer, L.: Theoretical foundations of potential func-tion method in pattern recognition learning. Autom. Remote Control 25, 821–837 (1964)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer India Pvt. Ltd.
About this paper
Cite this paper
Khare, S., Bhandari, A., Singh, S., Arora, A. (2012). ECG Arrhythmia Classification Using Spearman Rank Correlation and Support Vector Machine. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_54
Download citation
DOI: https://doi.org/10.1007/978-81-322-0491-6_54
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0490-9
Online ISBN: 978-81-322-0491-6
eBook Packages: EngineeringEngineering (R0)