Abstract
Long term Holter monitoring is widely applied to patients with heart diseases. Many of those diseases are not constantly present in the ECG signal but occurs from time to time. To detect these infrequent problems the Holter long time ECG recording is recorded and analysed. There are many methods for automatic detection of irregularities in the ECG signal. In this paper we will comapare the Support Vector Machine (SVM), J48 decision tree (J48), RBF artificial neural network (RBF), Simple logistic function and our novel GAME neural network for detection of the Premature Ventricular Contractions. We will compare and discuss classification performance of mentioned methods. There are also very many features which describes the ECG signal therefore we will try to identify features important for correct classification and examine how the accuracy is affected with only selected features in training set.
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References
Holter, N.J.: New method for heart studies. Science 134, 1214–1220 (1961)
de Chazal, P., O’Dweyr, O., Reilly, R.B.: Automatic classification of heartbeats using ECG morp. heartbeat intervals features. IEEE Trans. on Biom. Eng. 51(7), 1196–1206 (2004)
Cueasta-Frau, D., Perez-Cortes, J.C., Andreu-Garcia, G.: Clustering of electrocardiograph signals in computer-aided holter analysis. Computer methods and programs in Biomedicine 72, 179–196 (2003)
Moody, G., Mark, R.: Qrs morphology representation and noise estimation using the karhunen-loeve transform. Computer Cardiology 16, 269–272 (1989)
Lagerholm, M., Peterson, C., Edenbrandt, L., Sornmo, L.: Clustering ECG complexes using hermite functions and self-organizing maps. IEEE Trans. on Biom. Eng. 47, 838–848 (2000)
Christov, I., Herreto, G.G., Kraseva, V., Jekova, I., Gotchev, A., Egiazarian, K.: Comparative study of morphological and time-frequency ECG descriptos for heartbeat classification. Medical Engineering & Physics 28, 876–887 (2006)
Tsipouras, M.G., Voglis, C., Lagaris, I.E., Fotiadis, D.I.: Cardiac arrythmia classification using support vector machines. In: The 3rd European Medical and Biological Engineering Conference (2005)
Bortolan, G., Jekova, I., Christov, I.: Comparison of four methods for premature ventricular contraction and normal beat clustering. Computers in Cardiology 32, 921–924 (2005)
Hu, Y.H., Palreddy, S., Tompkins, W.J.: A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans. on Biom. Eng. 44, 891–900 (1997)
Goldberger, A.L., Amaral, L., Glass, L., Hausdorf, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G., Peng, C.K., Stanley, H.E.: Physiobank, physiotoolkit and physionet: Compnents of a new research resource for complex physiologic signals. Circulation 101(23), 215–220
The American Heart Association. AHA-Database. AHA Database Series 1, AHA database series, 1 edn. (1997)
Muller, J.A., Lemke, F.: Self-Organising Data Mining, Berlin (2000)
Madala, H., Ivakhnenko, A.: Inductive Learning Algorithm for Complex System Modelling. CRC Press, Boca Raton (1994)
Pavel KordÃk: Fully Automated Knowledge Extraction using Group of AdaptiveModels Evolution. PhD thesis, Czech Technical University, Prague (March 2007)
Witten, I., Frank, E.: Data Mining Practical Machine Learning Tools and Techniques. Elsevier, Amsterdam (2005)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Muller, K.R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. on Neural Networks 2(2), 181–201 (2001)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, Englewood Cliffs (1999)
Buhmann, M.D., Ablowitz, M.J.: Radial Basis Functions: Theory and Implementations. Cambridge University, Cambridge (2003)
Morganroth, J.: Premature ventricular complexes. diagnosis and indications for therapy. Journal of American Medical Association (1984)
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Čepek, M., Šnorek, M., Chudáček, V. (2008). ECG Signal Classification Using GAME Neural Network and Its Comparison to Other Classifiers. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_79
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DOI: https://doi.org/10.1007/978-3-540-87536-9_79
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