Identification of Premature Ventricular Contraction (PVC) Caused by Disturbances in Calcium and Potassium Ion Concentrations Using Artificial Neural Networks

Júlio César Dillinger Conway; Caroline Araújo Raposo; Sergio Diaz Contreras; Jadson Cláudio Belchior

doi:10.4236/health.2014.611162

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Health > Vol.6 No.11, May 2014

Identification of Premature Ventricular Contraction (PVC) Caused by Disturbances in Calcium and Potassium Ion Concentrations Using Artificial Neural Networks ()

Júlio César Dillinger Conway, Caroline Araújo Raposo, Sergio Diaz Contreras, Jadson Cláudio Belchior

Department of Chemistry—ICEx, Federal University of Minas Gerais, Belo Horizonte, Brazil.

DOI: 10.4236/health.2014.611162 PDF HTML XML 4,201 Downloads 5,374 Views Citations

Abstract

Abnormalities in the concentrations of metallic ions such as calcium and potassium can, in principle, lead to cardiac arrhythmias. Unbalance of these ions can alter the electrocardiogram (ECG) signal. Changes in the morphology of the ECG signal can occur due to changes in potassium concentration, and shortening or extension of this signal can occur due to calcium excess or deficiency, respectively. The diagnosis of these disorders can be complicated, making the modeling of such a system complex. In the present work an artificial neural network (ANN) is proposed as a model for pattern recognition of the ECG signal. The procedure can be, in principle, used to identify changes in the morphology of the ECG signal due to alterations in calcium and potassium concentrations. An arrhythmia database of a widely used experimental data was considered to simulate different ECG signals and also for training and validation of the methodology. The proposed approach can recognize premature ventricular contractions (PVC) arrhythmias, and tests were performed in a group of 47 individuals, showing significant quantitative results, on average, with 94% of confidence. The model was also able to detect ions changes and showed qualitative indications of what ion is affecting the ECG. These results indicate that the method can be efficiently applied to detect arrhythmias as well as to identify ions that may contribute to the development of cardiac arrhythmias. Accordingly, the actual approach might be used as an alternative tool for complex studies involving modifications in the morphology of the ECG signal associated with ionic changes.

Keywords

Arrhythmia, Calcium and Potassium Disorders, Electrocardiogram, Artificial Neural Network

Share and Cite:

Conway, J. , Raposo, C. , Contreras, S. and Belchior, J. (2014) Identification of Premature Ventricular Contraction (PVC) Caused by Disturbances in Calcium and Potassium Ion Concentrations Using Artificial Neural Networks. Health, 6, 1322-1332. doi: 10.4236/health.2014.611162.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Go, A.S., Mozaffarian D., Roger, V.L., Benjamin, E.J., Berry, J.D., Borden, W.B., Bravata, D.M., Dai, S., Ford, E.S., Fox, C.S., Franco, S., Fullerton, H.J., Gillespie, C., Hailpern, S.M., Heit, J.A., Howard, V.J., Huffman, M.D., Kissela, B.M., Kittner, S.J., Lackland, D.T., Lichtman, J.H., Lisabeth, L.D., Magid, D., Marcus, G.M., Marelli, A., Matchar, D.B., McGuire, D.K., Mohler, E.R., Moy, C.S., Mussolino, M.E., Nichol, G., Paynter, N.P., Schreiner, P.J., Sorlie, P.D., Stein, J., Turan, T.N., Virani, S.S., Wong, N.D., Woo, D., Turner, M.B., on Behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee (2013) Heart Disease and Stroke Statistics—2013 Update: A Report from the American Heart.
[2]	Dierks, D.B., Shumaik, G.M., Harrigan, R.A., Brady, W.J. and Chan, T.C. (2004) Electrocardiographic Manifestatios: Electrolyte Abnormalities. The Journal of Emergency Medicine, 27, 153-160. http://dx.doi.org/10.1016/j.jemermed.2004.04.006
[3]	Guyton, C. and Hall, J.E. (2000) Textbook of Medical Physiology. W. B. Sauders, Philadelphia, 122-129.
[4]	Engin, D., Fedakar, M., Engin, E.Z. and Korürek, M. (2006) Feature Measurements of ECG Beats Based on Statistical Classifiers. Measurement, 40, 904-912. http://dx.doi.org/10.1016/j.measurement.2006.10.012
[5]	Iigel, D.A. and Wilkofk, B.L. (1997) Automated Ventricular Tachyarrhythmia Recognition. Journal of Cardiovascular Electrophysiology, 8, 388-397. http://dx.doi.org/10.1111/j.1540-8167.1997.tb00804.x
[6]	Cabello, D., Barro, S., Salceda, J.M., Ruiz, R. and Mira, J. (1991) Fuzzy k-Nearest Neighbor Classifiers for Ventricular Arrhythmia Detection. International Journal of Bio-Medical Computing, 27, 77-93. http://dx.doi.org/10.1016/0020-7101(91)90089-W
[7]	Polat, K. and Gunes, S. (2007) Detection of ECG Arrhythmia Using a Differential Expert System Approach Based on Principal Component Analysis and Least Square Support Vector Machine. Applied Mathematics and Computation, 186, 898-906. http://dx.doi.org/10.1016/j.amc.2006.08.020
[8]	Owis, M.I., Abou-Zied, A.H., Youssef, A.B.M. and Kadah, Y.M. (2002) Study of Features Based on Nonlinear Dynamical Modeling in ECG Arrhythmia Detection and Classification. IEEE Transactions in Biomedical Engineering, 49, 733-736. http://dx.doi.org/10.1109/TBME.2002.1010858
[9]	Chen, S.W., Clarkson, P.M. and Fan, Q. (1996) A Robust Sequential Detection Algorithm for Cardiac Arrhythmia Classification. IEEE Transactions on Biomedical Engineering, 43, 1120-1125. http://dx.doi.org/10.1109/10.541254
[10]	Khare, V., Santhosh, J., Anand, S. and Bhatia, M. (2009) Performance Comparison of Neural Network Training Methods Based on Wavelet Packet Transform for Classification of Five Mental Tasks. Journal of Biomedical Science and Engineering, 3, 612-617. http://dx.doi.org/10.4236/jbise.2010.36083
[11]	Mohammadi, H., Nemati, M., Allahmoradi, Z., Raissi, H.F., Esmaili, S.S. and Sheikhani, A. (2011) Ultrasound Estimation of Fetal Weight in Twins by Artificial Neural Networks. Journal of Biomedical Science and Engineering, 4, 46-50. http://dx.doi.org/10.4236/jbise.2011.41006
[12]	Balbinot, A., Júnior, A.S. and Favieiro, G.W. (2013) Decoding Arm Movements by Myoelectric Signal and Artificial Neural Networks. Intelligent Control and Automation, 4, 87-93. http://dx.doi.org/10.4236/ica.2013.41012
[13]	Engin, M. (2004) ECG Beat Classification Using Neuro-Fuzzy Network. Pattern Recognition Letters, 25, 1715-1722. http://dx.doi.org/10.1016/j.patrec.2004.06.014
[14]	Zhou, J. (2003) Automatic Detection of Premature Ventricular Contraction Using Quantum Neural Networks. Proceedings of 3rd IEEE Symposium on BioInformatics and BioEngineering. IEEE Computer Society, 3, 169-173.
[15]	Zhou, J. and Li, L. (2004) Regularized B-Spline Network and Its Application to Heart Arrhythmia Classification. Proceedings of the 2004 ACM Symposium on Applied Computing, Nicosia, 14-17 March 2004, 291-295.
[16]	Ubeyli, E.D. (2008) Implementing Wavelet Transform/Mixture of Experts Network for Analysis of Electrocardiogram Beats. Expert Systems, 25, 150-162. http://dx.doi.org/10.1016/j.patrec.2004.06.014
[17]	Lin, H., Du, Y.C. and Chen, T. (2006) Adaptive Wavelet Network for Multiple Cardiac Arrhythmias Recognition. Expert Systems with Applications, 34, 2601-2611. http://dx.doi.org/10.1016/j.eswa.2007.05.008
[18]	Gothwal, H., Kedawat, S. and Kumar, R. (2011) Cardiac Arrhythmias Detection in an ECG Beat Signal Using Fast Fourier Transform and Artificial Neural Network. Journal of Biomedical Science and Engineering, 4, 289-296. http://dx.doi.org/10.4236/jbise.2011.44039
[19]	Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C.H., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K. and Stanley, H.E. (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101, 215-220. http://dx.doi.org/10.1161/01.CIR.101.23.e215
[20]	Beer, M.H., Porter, R.S., Jones, T.V., Kaplan, J.L. and Berkwits, M., Eds. (2006) The Merk Manual of Diagnosis and Therapy. 18th Edition, Merk Research Laboratories, Whitehouse Station.
[21]	Fisch, C. (1973) Relation of Electrolyte Disturbances to Cardiac Arrhythmias. Circulation, 47, 408-419. http://dx.doi.org/10.1161/01.CIR.47.2.408
[22]	Goldemberg, I., Moss, A.J. and Zareba, W. (2006) Clinical Course and Risk Stratification of Patients Affected with the Jervell and Lange-Nielsen Syndrome. Journal of Cardiovascular Electrophysiology, 17, 1161-1168. http://dx.doi.org/10.1111/j.1540-8167.2006.00587.x
[23]	Bazett, H.C. (1997) An Analysis of Time-Relations of Electrocardiograms. Annals of Noninvasive Electrocardiology, 2, 177-194. http://dx.doi.org/10.1111/j.1542-474X.1997.tb00325.x
[24]	Haykin, S. (1999) Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River.