Diagnosis of long QT syndrome via support vector machines classification

DOI: 10.4236/jbise.2011.44036   PDF   HTML     3,663 Downloads   7,167 Views   Citations


Congenital Long QT Syndrome (LQTS) is a genetic disease and associated with significant arrhythmias and sudden cardiac death. We introduce a noninva-sive procedure in which Discrete Wavelet Trans-form (DWT) is used to extract features from elec-trocardiogram (ECG) time-series data first, then the extracted features data is classified as either abnormal or unaffected using Support Vector Machines (SVM). A total of 26 genetically identified patients with LQTS and 19 healthy controls were studied. Due to the limited number of samples, model selection was done by training 44 instances and testing it on remaining one in each run. The proposed method shows reasonably high average accuracy in LQTS diagnosis when combined with best parameter selection process in the classifica-tion stage. An accuracy of 80%is achieved when Sigmoid kernel is used in v-SVM with parameters v = 0.58 and r = 0.5. The corresponding SVM model showed a classification rate of 21/26 for LQTS pa-tients and 15/19 for controls. Since the diagnosis of LQTS can be challenging, the proposed method is promising and can be a potential tool in the correct diagnosis. The method may be improved further if larger data sets can be obtained and used.

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Bisgin, H. , Kilinc, O. , Ugur, A. , Xu, X. and Tuzcu, V. (2011) Diagnosis of long QT syndrome via support vector machines classification. Journal of Biomedical Science and Engineering, 4, 264-271. doi: 10.4236/jbise.2011.44036.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Chang, C.C. and Lin, C.J. (2001) LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
[2] Polikar, R. (2011) The wavelet tutorial.
[3] Alpaydin, E. (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, Cambridge, USA.
[4] Burrus, C.S., Gopinath, R.A. and Guo, H. (1998) Introduction to Wavelets and Wavelet Transforms: A Primer. Prentice Hall, Inc., New Jersey, USA.
[5] Chan, K. and Fu, A.W. (1999) Efficient time series matching by wavelets. In: ICDE, 12-133.
[6] Couderc, J.P. and Zareba, W. (1998) Contribution of the wavelet analysis to the noninvasive electrocardiology. Annals of Noninvasive Electrocardiology, 3, 54-62. doi:10.1111/j.1542-474X.1998.tb00030.x
[7] Jensen, B.T. (2004) Beat-to-beat QT dynamics in healthy subjects. Annals of Noninvasive Electrocardiology, 9, 3-11. doi:10.1111/j.1542-474X.2004.91510.x
[8] Li, Y. and Zhang, C. (1993) QRS detection by wavelet transform. Proceedings of Conference of IEEE Engineering in Medicine and Biology, 15, 330-331.
[9] Mallat, S.G. (1989) A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 674-693. doi:10.1109/34.192463
[10] M?rchen, F. (2003) Time series feature extraction for data mining using DWT and DFT. Data Bionics, Philipps-University Marburg, Germany, 1-31.
[11] Perk?maki, J.S., Zareba, W., Nomura, A., Andrews, M., Kaufman, E.S. and Moss, A.J. (2002) Repolarization dynamics in patients with long QT syndrome. Journal of Electrophysilogy, 13, 651-656.
[12] Sch?lkopf, B. and Smola, A.J. (2002) Learning with Kernels: Support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge.
[13] Sch?lkopf, B., Smola, A.J., Williamson, R.C. and Bartlett, P.L. (2000) New support vector algorithms. Neural Computation, 12, 1207-1245.
[14] Schwartz, P.J., Moss, A.J., Vincent, G.M. and Crampton, R.S. (1993) Diagnostic criteria for the long QT syndrome. An update. Journal of the American Heart Association, 88, 782-784.
[15] Schwartz, P.J., Stramba-Badiale, M., Crotti, L., Pedrazzini, M., Besana, A., Bosi, G., Gabbarini, F., Goulene, K., Insolia, R., Mannarino, S., Mosca, F., Nespoli, L., Rimini, A., Rosati, E., Salice, P. and Spazzolini, C. (2009) Prevalence of the congenital long-QT syndrome. Circulation, 120, 1761-1767. doi:10.1161/CIRCULATIONAHA.109.863209
[16] Strachan, I.G.D., Hughes, N.P., Poonawala, M.H., Wason, J.W. and Tarassenko, L. (2009) Automated QT analysis that learns from cardiologist annotations. Annals of Noninvasive Electrocardiology, Supplement 1, 9-21. doi:10.1111/j.1542-474X.2008.00259.x
[17] Terrence, S.F., Nello, C., Nigel, D., David, W., Schummer, M. and Haussler, D. (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16, 906-914.
[18] Witten, I.H. and Frank, E. (2005) Data mining practical machine learning tools and techniques. Morgan Kaufman, San Francisco, USA.
[19] Wong, S.N., Mora, F., Passariello, G. and Almeida, D. (1998) QT interval time frequency analysis using Haar wavelet. Computers in Cardiology, 25, 405-408.
[20] Wu, Y., Agrawal, D. and Abbadi, A.E. (2000) A comparison of DFT and DWT based similarity search in time-series databases. Proceedings of the 9th International Conference on Information and Knowledge Management, McLean, November 2009, 488-495.

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