Abstract
In this work, we are focusing on the problem of heartbeat classification in electrocardiogram (ECG) signals. First we develop a patient-specific feature extraction scheme by using adaptive orthogonal transformations based on wavelets, B-splines, Hermite and rational functions. The so-called variable projection provides the general framework to find the optimal nonlinear parameters of these transformations. After extracting the features, we train a support vector machine (SVM) for each model whose outputs are combined via ensemble learning techniques. In the experiments, we achieved an accuracy of \(94.2\%\) on the PhysioNet MIT-BIH Arrhythmia Database that shows the potential of the proposed signal models in arrhythmia detection.
EFOP-3.6.3-VEKOP-16-2017-00001: Talent Management in Autonomous Vehicle Control Technologies – The Project is supported by the Hungarian Government and co-financed by the European Social Fund.
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References
Testing and Reporting Performance Results of Cardiac Rhythm and ST-Segment Measurement Algorithms. American National Standard, ANSI/AAMI/ISO EC57. 1998-(R) (2008)
Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, 215–220 (2000)
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20, 45–50 (2001)
Gabbouj, M., Karczewicz, M.: ECG data compression by spline approximation. Signal Process. 59, 43–59 (1997)
Kovács, P., Fekete, A.M.: Nonlinear least-squares spline fitting with variable knots. Appl. Math. Comput. 2019(354), 490–501 (2019)
Dózsa, T., Kovács, P.: ECG signal compression using adaptive hermite functions. In: Loshkovska, S., Koceski, S. (eds.) ICT Innovations 2015. AISC, vol. 399, pp. 245–254. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25733-4_25
Fridli, S., Lócsi, L., Schipp, F.: Rational function systems in ECG processing. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2011. LNCS, vol. 6927, pp. 88–95. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27549-4_12
Fridli, S., Kovács, P., Lócsi, L., Schipp, F.: Rational modeling of multi-lead QRS complexes in ECG signals. Ann. Univ. Sci. Budapest Sect. Comp. 37, 145–155 (2012)
Bognár, G., Fridli, S.: Heartbeat classification of ECG signals using rational function systems. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2017. LNCS, vol. 10672, pp. 187–195. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74727-9_22
Golub, G.H., Pereyra, V.: The differentiation of pseudo-inverses and nonlinear least squares problems whose variables separate. SIAM J. Numer. Anal. 10, 413–432 (1973)
Ye, C., Kumar, B.V.K.V., Coimbra, M.T.: Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans. Biomed. Eng. 59, 2930–2941 (2012)
de Chazal, P., O’Dwyer, M., Reilly, B.R.: Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51, 1196–1206 (2004)
Szegő, G.: Orthogonal Polynomials, 3rd edn. AMS Colloquium Publications, New York (1967)
Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A Practical Guide to Support Vector Machine Classification. National Taiwan University, pp. 1–16 (2016)
Llamendo, M., Khawaja, A., Martínez, J.P.: Cross-database evaluation of a multilead heartbeat classifier. IEEE Trans. Inf. Technol. Biomed. 16, 658–664 (2012)
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Dózsa, T., Bognár, G., Kovács, P. (2020). Ensemble Learning for Heartbeat Classification Using Adaptive Orthogonal Transformations. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12014. Springer, Cham. https://doi.org/10.1007/978-3-030-45096-0_44
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