Özal YILDIRIM, Ulaş Baran BALOĞLU

Heartbeat type classification with optimized feature vectors

In this study, a feature vector optimization based method has been proposed forclassification of the heartbeat types. Electrocardiogram (ECG) signals of fivedifferent heartbeat type were used for this aim. Firstly, wavelet transform (WT)method were applied on these ECG signals to generate all feature vectors.Optimizing these feature vectors is provided by performing particle swarmoptimization (PSO), genetic search, best first, greedy stepwise and multi objectiveevoluationary algorithms on these vectors. These optimized feature vectors arelater applied to the classifier inputs for performance evaluation. A comprehensiveassessment was presented for the determination of optimized feature vectors forECG signals and best-performing classifier for these optimized feature vectors wasdetermined.

PDF

___

Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE transactions on biomedical engineering, (3), 230-236.
Okada, M. (1979). A digital filter for the ors complex detection. IEEE Transactions on Biomedical Engineering, (12), 700-703.
Afonso, V. X., Tompkins, W. J., Nguyen, T. Q., & Luo, S. (1999). ECG beat detection using filter banks. IEEE transactions on biomedical engineering, 46(2), 192-202.
Li, C., Zheng, C., & Tai, C. (1995). Detection of ECG characteristic points using wavelet transforms. IEEE Transactions on biomedical Engineering, 42(1), 21-28.
Rekik, S., & Ellouze, N. (2016). QRS detection combining entropic criterion and wavelet transform. International Journal of Signal and Imaging Systems Engineering, 9(4-5), 299-304.
Rani, R., Chouhan, V. S., & Sinha, H. P. (2015). Automated detection of qrs complex in ECG signal using wavelet transform. International Journal of Computer Science and Network Security (IJCSNS), 15(1), 1.
Kaur, I., Rajni, R., & Marwaha, A. (2016). ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform. Journal of The Institution of Engineers (India): Series B, 97(4), 499-507.
Chen, T. H., Zheng, Y., Han, L. Q., Guo, P. Y., & He, X. Y. (2008). The Sorting Method of ECG Signals Based on Neural Network. In Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on (pp. 543-546). IEEE.
Dokur, Z., Ölmez, T., Yazgan, E., & Ersoy, O. K. (1997). Detection of ECG waveforms by neural networks. Medical engineering & physics, 19(8), 738-741.
Coast, D. A., Stern, R. M., Cano, G. G., & Briller, S. A. (1990). An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Transactions on biomedical Engineering, 37(9), 826-836.
Jain, S., Kumar, A., & Bajaj, V. (2016). Technique for QRS complex detection using particle swarm optimisation. IET Science, Measurement & Technology, 10(6), 626-636.
Thomas, M., Das, M. K., & Ari, S. (2015). Automatic ECG arrhythmia classification using dual tree complex wavelet based features. AEUInternational Journal of Electronics and Communications, 69(4), 715-721..
Inan, O. T., Giovangrandi, L., & Kovacs, G. T. (2006). Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Transactions on Biomedical Engineering, 53(12), 2507-2515.
Sahoo, S., Kanungo, B., Behera, S., & Sabut, S. (2017). Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement, 108, 55-66.
Martis, R. J., Acharya, U. R., & Min, L. C. (2013). ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomedical Signal Processing and Control, 8(5), 437-448.
Ince, T., Kiranyaz, S., & Gabbouj, M. (2009). A generic and robust system for automated patientspecific classification of ECG signals. IEEE Transactions on Biomedical Engineering, 56(5), 1415-1426.
Martis, R. J., Acharya, U. R., Mandana, K. M., Ray, A. K., & Chakraborty, C. (2013). Cardiac decision making using higher order spectra. Biomedical Signal Processing and Control, 8(2), 193-203.
Tadejko, P., & Rakowski, W. (2007). Mathematical morphology based ECG feature extraction for the purpose of heartbeat classification. In Computer Information Systems and Industrial Management Applications, 6th International Conference on (pp. 322-327). IEEE.
Kim, J., Shin, H., Lee, Y., & Lee, M. (2007). Algorithm for classifying arrhythmia using Extreme Learning Machine and principal component analysis. In Engineering in Medicine and Biology Society, EMBS 2007. 29th Annual International Conference of the IEEE (pp. 3257-3260). IEEE.
Martis, R. J., Acharya, U. R., Mandana, K. M., Ray, A. K., & Chakraborty, C. (2012). Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Systems with Applications, 39(14), 11792-11800.
Mehta, S. S., & Lingayat, N. S. (2008). Development of SVM based ECG Pattern Recognition Technique. IETE Journal of Research, 54(1), 5-11.
Raman, P., & Ghosh, S. (2016). Classification of Heart Diseases based on ECG analysis using FCM and SVM Methods. International Journal of Engineering Science, 6739.
Ceylan, R., & Özbay, Y. (2007). Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network. Expert Systems with Applications, 33(2), 286-295.
Shadmand, S., & Mashoufi, B. (2016). A new personalized ECG signal classification algorithm using block-based neural network and particle swarm optimization. Biomedical Signal Processing and Control, 25, 12-23.
Güler, İ., & Übeylı, E. D. (2005). ECG beat classifier designed by combined neural network model. Pattern recognition, 38(2), 199-208.
Moraglio, A., Di Chio, C., & Poli, R. (2007). Geometric particle swarm optimisation. In European conference on genetic programming (pp. 125-136). Springer, Berlin, Heidelberg.
Gutlein, M., Frank, E., Hall, M., & Karwath, A. (2009). Large-scale attribute selection using wrappers. In Computational Intelligence and Data Mining, IEEE Symposium on (pp. 332-339). IEEE.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
Jiménez, F., Sánchez, G., García, J. M., Sciavicco, G., & Miralles, L. (2017). Multi-objective evolutionary feature selection for online sales forecasting. Neurocomputing, 234, 75-92.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293-300.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157-1182..
Martín-Smith, P., Ortega, J., Asensio-Cubero, J., Gan, J. Q., & Ortiz, A. (2017). A supervised filter method for multi-objective feature selection in EEG classification based on multi-resolution analysis for BCI. Neurocomputing, 250, 45-56.
Mark, R. Moody, G. (1997). MIT-BIH Arrhythmia Database, http://ecg.mit.edu/dbinfo.html