Volume 18, Issue 2 (10-2021)
JSDP 2021, 18(2): 163-176 |
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Vaghefi M, Jamshidi F. Features selection for cardiac arrhythmia diagnosis using multiple objective binary particle swarm optimization. JSDP 2021; 18 (2) :163-176
URL: http://jsdp.rcisp.ac.ir/article-1-972-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-972-en.html
Department of Electrical Engineering, Shiraz Branch, Islamic Azad University
Abstract: (1890 Views)
Any heart activity disorder may lead an irregularity in is rhythm, or cardiac arrhythmia. An ECG signal is one of the major tools for classifying different types of cardiac arrhythmias. ECG signals usually contain various noises. To have a better signal processing, it is essential to remove noises in a way that a signal structure never becomes subject to distortion. After the step of noise removal, selection of an appropriate method is of paramount importance for feature extraction. Optimal features can be selected to improve efficiency and reduce calculations. This article used the ensemble empirical mode decomposition (EEMD) in which any intrinsic mode function (IMF) contains only a single frequency component for noise removal. The noise removal operation with the least distortion is possible using an appropriate windowing on a QRS complex containing sum of the first three IMFs. Later, the remaining noises can be removed using discrete wavelet transform (DWT). The results of using the EEMD-DWT combined method were compared with EMD and DWT combination. After the noise removal step, feature extraction was performed through a wavelet packet decomposition. It is capable of signal decomposition at all frequencies. Multiple objective binary particle swarm optimization (MOBPSO) method was used to select optimal features and the effect of this method on the results was examined. Finally, the back propagation neural network (BPNN) and a support vector machine based on particle swarm optimization were used for classification. This article used 17 signals received from the MIT-BIH database. The acquired data belong to 6 different types of classes. After pre-processing, feature extraction, feature selection, and classification on the input data, it is observed that the proposed technique of EEMD-DWT is an appropriate method for noise removal and MOBPSO is a suitable method for the selection of best features. The BPNN classifier managed to classify cardiac arrhythmias with a higher accuracy and the values for accuracy, sensitivity, specificity, and positive predictive value were 99.12%, 97.08%, 99.38%, and 97.12%, respectively.
Keywords: Back Propagation Neural Network, Discrete Wavelet Transform, Electrocardiogram, Ensemble Empirical Mode Decomposition, Multiple Objective Binary Particle Swarm Optimization
Type of Study: Research |
Subject:
Paper
Received: 2019/02/14 | Accepted: 2020/08/18 | Published: 2021/10/8 | ePublished: 2021/10/8
Received: 2019/02/14 | Accepted: 2020/08/18 | Published: 2021/10/8 | ePublished: 2021/10/8
References
1. [1] A. Guyton, H. Hall, "Medical Physiology", Translated by F. Shadan, Tehran, Chehr Publication, Vol. I, 2010.
2. [2]. Md. A. Kabir and C. Shahnaz, "Denoising of ECG Signals Based on Noise Reduction Algorithms in EMD and Wavelet Domains," Biomedical Signal Processing and Control, vol. 7, no. 5, pp. 481- 489, 2012. [DOI:10.1016/j.bspc.2011.11.003]
3. [3]. N. Li and P. Li, "An Improved Algorithm Based on EMD-Wavelet for ECG Signal De-noising," In: International Joint Conference on Computational Sciences and Optimization, Sanya, pp. 825-827, 2009.
4. [4]. K. M. Chang, "Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition," Sensors, vol. 10, no. 6, pp. 6063-6080, 2010. [DOI:10.3390/s100606063] [PMID] [PMCID]
5. [5]. S. Poungponsri and X. H. Yu, "An Adaptive Filtering Approach for Electrocardiogram (ECG) Signal Noise Reduction Using Neural Networks," Neurocomputing, vol. 117, pp. 206- 213, 2013. [DOI:10.1016/j.neucom.2013.02.010]
6. [6]. H. Y. Lin, S. Y. Liang, Y. L. Ho, Y. H. Lin and H. P. Ma, "Discrete-Wavelet-Transform-Based Noise Removal and Feature Extraction for ECG Signals," IRBM, vol. 35, no. 6, pp. 351-361, 2014. [DOI:10.1016/j.irbm.2014.10.004]
7. [7]. V. Joshi, A. R. Verma and Y. Singh, "De-noising of ECG Signal Using Adaptive Filter Based on MPSO," Procedia Computer Science, vol. 57, pp. 395-402, 2015. [DOI:10.1016/j.procs.2015.07.354]
8. [8]. S. Karpagachelvi, M. Arthanari and M. Sivakumar, "ECG Feature Extraction Techniques - A Survey Approach," International Journal of Computer Science and Information Security, vol. 8, no. 1, 2010, arXiv: 1005.0957.
9. [9]. S. Coumo, G. De. Pietro, R. Farina, A. Galletti and G. Sannino, "A Revised Scheme for Real Time ECG Signal Denoising Based on Recursive Filtering", Biomedical Signal Processing and Control, vol. 27, pp. 134-144, 2016. [DOI:10.1016/j.bspc.2016.02.007]
10. [10]. M. Korurek and A. Nizam, "Clustering MIT-BIH arrhythmias with ant colony optimization using time domain and PCA compressed wavelet coefficients," Digital Signal Processing, vol. 20, no. 4, pp. 1050-1060, 2010. [DOI:10.1016/j.dsp.2009.10.019]
11. [11]. N. Acir, "Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm," Neural Computing & Applications, vol. 14, no. 4, pp. 299-309, 2005. [DOI:10.1007/s00521-005-0466-z]
12. [12]. P. de Chazal, M. O'Dwyer and R. B. Reilly, "Automatic classification of heartbeats using ECG morphology and heartbeat interval features", IEEE Transactions on Biomedical Engineering, vol. 51, no. 7, pp. 1196-1206, 2004. [DOI:10.1109/TBME.2004.827359] [PMID]
13. [13]. M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt and L. Sornmo, "Clustering ECG complexes using hermite functions and self-organizing maps," IEEE Transactions on Biomedical Engineering, vol. 47, no. 7, pp. 838-848, 2000. [DOI:10.1109/10.846677] [PMID]
14. [14]. L. Senhadji, J. J. Bellanger, G. Carrault and G. Passariello, "Comparing wavelet transforms for recognizing cardiac patterns," IEEE Engineering in Medicine and Biology Magazine, vol. 14, no. 2, pp. 167-173, 1995. [DOI:10.1109/51.376755]
15. [15]. R. Rodriguez, A. Mexicano, J. Bila, S. Cervantes and R. Ponce, "Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis," Journal of Applied Research and Technology, vol. 13, no. 2, pp. 261-269, 2015. [DOI:10.1016/j.jart.2015.06.008]
16. [16]. A. Ghaheri, S. Shoar, M. Naderan and S. S. Hoseini, "The Applications of Genetic Algorithms in Medicine," Oman Medical Journal, vol. 30, no. 6, pp. 406-416, 2015. [DOI:10.5001/omj.2015.82] [PMID] [PMCID]
17. [17]. Y. Kutlua and D. Kuntalp, "Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients," Computer Methods and Programs in Biomedicine, vol. 105, no. 3, pp. 257-267, 2012. [DOI:10.1016/j.cmpb.2011.10.002] [PMID]
18. [18]. P. Kora and K. S. R. krishna, "Hybrid Firefly and Particle Swarm Optimization algorithm for the detection of Bundle Branch Block," International Journal of the Cardiovascular Academy, vol. 2, no. 1, pp. 44-48, 2016. [DOI:10.1016/j.ijcac.2015.12.001]
19. [19]. J. S. Wang, C. W. Lin and Y. T. C. Yang, "A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition," Neurocomputing, vol. 116, pp.136-143, 2013. [DOI:10.1016/j.neucom.2011.10.047]
20. [20]. H. Li, D. Yuan, X. Ma, D. Cui and L. Cao, "Genetic Algorithm for the Optimization of Features and Neural Networks in ECG Signals Classification," Scientific Reports, vol. 7, pp. 41011, 2017, doi: 10.1038/srep41011. [DOI:10.1038/srep41011] [PMID] [PMCID]
21. [21]. P. R. Bokde, "An ECG Beat Classification Using Adaptive Neuro-Fuzzy Inference System," International Research Journal of Advanced Engineering and Science, vol. 2. No. 2, pp. 354-358, 2017.
22. [22]. P. Bhardwaj, R. R. Choudhary and R. Dayama, "Analysis and Classification of Cardiac Arrhythmia Using ECG Signals," International Journal of Computer Applications, vol. 38, no. 1, pp. 37-40, 2012. [DOI:10.5120/4575-6742]
23. [23]. M. Rahbaripour and B. Mohammadzadeh Asl. "Premature Ventricular Contraction Arrhythmia Detection in ECG Signals via Combined Classifiers". JSDP. 2018; 15 (1) :55-70 [DOI:10.29252/jsdp.15.1.55]
24. [24]. J. Rodriguez, A. Goni and A. Illarramendi, "Real-Time Classification of ECGs on a PDA," IEEE Transactions on Information Technology in Biomedicine, vol. 9, no. 1m pp. 23-34, 2005. [DOI:10.1109/TITB.2004.838369] [PMID]
25. [25]. S. Kiranyaz, T. Ince and M. Gabbouj, "Real-Time Patient-Specific ECG Classification by 1D Convolutional Neural Networks," IEEE Transactions on Biomedical Engineering, vol. 63, no. 3, pp. 664-675, 2016. [DOI:10.1109/TBME.2015.2468589] [PMID]
26. [26]. Y. Zhang, Y. Zhang, B. Lo, W. Xu, "Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection," John Wiley & Sons, Ltd, 2019, DOI: 10.1111/exsy.12432. [DOI:10.1111/exsy.12432]
27. [27]. G. B. Moody and R. G. Mark, "The Impact of the MIT-BIH Arrhythmia Database," IEEE Engineering in Medicine and Biology Society, vol. 20, no. 3, pp. 45-50, 2001. [DOI:10.1109/51.932724] [PMID]
28. [28]. P. Nguyen and J. M. Kim, "Adaptive ECG Denoising Using Genetic Algorithm-Based Thresholding and Ensemble Emprical Mode Decomposition," Information Sciences, vol. 373, pp. 499-511, 2016. [DOI:10.1016/j.ins.2016.09.033]
29. [29]. S. G. Chang, B. Yu and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," IEEE Transactions on Image Processing, vol. 9, no. 9, pp. 1532-1546, 2000. [DOI:10.1109/83.862633] [PMID]
30. [30]. Y. Kutlu and D. Kuntalp, "Feature Extraction for ECG Heartbeats Using Higher Order Statistics of WPD Coefficients," Computer Methods and Programs in Biomedicine, vol. 105, no. 3, pp. 257-267, 2012. [DOI:10.1016/j.cmpb.2011.10.002] [PMID]
31. [31]. S. Mirjalili and A. Lewis, "S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization," Swarm and Evolutionary Computation, vol. 9, pp. 1-14, 2013. [DOI:10.1016/j.swevo.2012.09.002]
32. [32]. R. Parikh, A. Mathai, S. Parikh, G. C. Sekhar and R. Thomas, "Understanding and using sensitivity, specificity and predictive values," Indian Journal of Ophthalmology, vol. 56, no. 1, pp. 45-50, 2008. [DOI:10.4103/0301-4738.37595] [PMID] [PMCID]
33. [33]. S. S. Mehta and N. S. Lingayat, "SVM-Based Algorithm for Recognition of QRS Complexes in Electrocardiogram," IRBM, vol. 29, pp. 310-317, 2008. [DOI:10.1016/j.rbmret.2008.03.006]
34. [34]. J. Saeedi, S. M. Ahadi and K. Faez, "Robust voice activity detection directed by noise classification. Signal," Image and Video Processing, vol. 9, no. 3, pp. 561-572, 2013. [DOI:10.1007/s11760-013-0479-5]
35. [35]. S. Dutta, A. Chatterjee and S. Munshi, "Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification," Medical Engineering & Physics, vol. 32, no. 10, pp. 1161-1169, 2010. [DOI:10.1016/j.medengphy.2010.08.007] [PMID]
36. [36]. W. Jatmiko, W. P. Nulad, I. E. Matul, I. M. A. Setiawan and P. Mursanto, "Heart Beat Classification Using Wavelet Feature Based on Neural Network," WSEAS Transactions on Systems, vol. 10, no. 1, pp. 17-26, 2011.
37. [37]. A. Ebahimzadeh, B. Shakiba and A. Khazaee, "Detection of electrocardiogram signals using an efficient method," Applied Soft Computing, vol. 22, pp. 108-117, 2014. [DOI:10.1016/j.asoc.2014.05.003]
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