Abstract
Correct and accurate classification of ECG patterns in a long-term record requires optimal selection of feature vector. We propose a machine learning algorithm that learns from short randomly selected signal strips and, having an approval from a human operator, classifies all remaining patterns. We applied a genetic algorithm with aggressive mutation to select few most distinctive features of ECG signal. When applied to the MIT-BIH Arrhythmia Database records, the algorithm reduced the initial feature space of 57 elements to 3–5 features optimized for a particular subject. We also observe a significant reduction of misclassified beats percentage (from 2.7 % to 0.7 % in average for SVM classifier and three features) with regard to automatic correlation-based selection.
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References
Augustyniak, P.: The use of shape factors for heart beats classification in holterrecordings. Proc. Comput. Med. Zakop. 2–6(05), 47–52 (1997)
Augustyniak, P.: Adaptive discrete ECG representation—comparing variable depth decimation and continuous non-uniform sampling. Comput. Cardiol. 29, 165–168 (2005)
Augustyniak, P.: Wearable wireless heart rate monitor for continuous long-term variability studies. J. Electrocardiol. 44(2), 195–200 (2011)
Chang, K.C., Lee, R.G., Wen, C., Yeh, M.F.: Comparison of similarity measures for clustering electrocardiogram complexes. Comput. Cardiol. 32, 759–762 (2005)
de Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196–1206 (2004)
Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. ACM SIGART Bull. 63, 43–49 (1977)
Jaworek, J., Augustyniak, P.: A cardiac telerehabilitation application for mobile devices. Comput. Cardiol. 38, 241–244 (2011)
Jokić, S., Krčo, S., Delić, V., Sakač, D., Lukić, Z., Loncar-Turukalo, T.: An efficient approach for heartbeat classification. Comput. Cardiol. 2010(37), 991–994 (2010)
Kittler, J.: Feature set search algorithms. In: Pattern Recognition and Signal Processing, pp. 41–60. Sijthoff and Noordhoff, Alphen aan den Rijn (1978)
Kononenko, I.: Estimating attributes: analysis and extensions of relief. In: De Raedt, L., Bergadano, F. (eds.) Machine Learning: ECML-94, pp. 171–182. Springer, Berlin (1994)
Lemay, M., Jacquemet, V., Forclaz, A., Vesin, J.M., Kappenberger, L.: Spatiotemporal QRST cancellation method using separate QRS and T-Waves templates. Comput. Cardiol. 32, 611–614 (2005)
Llamedo-Soria, M., Martinez, J.P.: An ECG classification model based on multilead wavelet transform features. Comput. Cardiol. 34, 105–108 (2007)
Llamedo-Soria, M., Martinez, J.P.: Analysis of multidoma in features for ECG classification. Comput. Cardiol. 36, 561–564 (2009)
Llamedo, M., Khwaja, A., Martinez, J.P.: Analysis of 12-lead classification models for ECG classification. Comput. Cardiol. 37, 673–676 (2010)
Llamedo, M., Martinez, J.: Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans. Biomed. Eng. 58(3), 616–625 (2011)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Le Cam, L.M., Neyman, J. (eds.) Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Mallat, S., Zhong, S.: Characterization of signals from multiscale edges. IEEE Trans. Pattern Anal. Mach. Intell. 14(7), 710–732 (1992)
Mensing, S., Bystricky, W., Safer, A.: Identifying and measuring representative QT intervals in predominantly non-normal ECGs. Comput. Cardiol. 33, 361–364 (2006)
Moody, G.B.: The MIT-BIH Arrhythmia Database CD-ROM, 3rd Edn. Harvard-MIT Division of Health Sciences and Technology, Cambridge (1997)
O’Dwyer, M., de Chazal, P., Reilly, R.I.: Beat classification for use in arrhythmia analysis. Comput. Cardiol. 27, 395–398 (2000)
Rejer, I.: Genetic algorithms in EEG feature selection for the classification of movements of the left and right hand. In: Proceedings CORES2013, pp. 579–589 (2013). doi:10.1007/978-3-319-00969-8-57
Rejer, I.: Genetic algorithm with aggressive mutation for feature selection in BCI feature space pattern. Anal. Appl. (2014). doi:10.1007/s10044-014-0425-3
Rodriguez-Sotelo, J.L., Cuesta-Frau, D., Castellanos-Dominguez, G.: An improved method for unsupervised analysis of ECG beats based on WT features and J-Means clustering. Comput. Cardiol. 34, 581–584 (2007)
Tibshirani, R.: Regression shrinkage and selection via thelasso. J. Stat. Soc. Ser. B 58(1), 267–288 (1996)
Vansteenkiste, E., Houben, R., Pizurica, A., Philips, W.: Classifying electrocardiogram peaks using new wavelet domain features. Comput. Cardiol. 35, 853–856 (2008)
Vapnik, V.N.: The Nature Of Statistical Learning Theory. Springer, New York (1995)
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This scientific work is supported by the AGH University of Science and Technology in year 2015 as a research project No. 11.11.120.612.
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Augustyniak, P. (2016). Accurate Classification of ECG Patterns with Subject-Dependent Feature Vector. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_50
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