Volume 15, Issue 1 (6-2018)                   JSDP 2018, 15(1): 55-70 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Rahbaripour M, Mohammadzadeh Asl B. Premature Ventricular Contraction Arrhythmia Detection in ECG Signals via Combined Classifiers. JSDP 2018; 15 (1) :55-70
URL: http://jsdp.rcisp.ac.ir/article-1-584-en.html
Tarbiat Modares University
Abstract:   (5660 Views)

Cardiovascular diseases are the most dangerous diseases and one of the biggest causes of fatality all over the world. One of the most common cardiac arrhythmias which has been considered by physicians is premature ventricular contraction (PVC) arrhythmia. Detecting this type of arrhythmia due to its abundance of all ages, is particularly important. ECG signal recording is a non-invasive, popular method for an assessment of heart's function. Development of quick, accurate automatic ECG classification methods is essential for the clinical diagnosis of heart disease. This research analyzes the ECG signal to detect PVC arrhythmia. Different techniques are provided in order to detect this type of arrhythmia based on ECG signals. As these techniques use different methods for detection, the reaction of each one will be different to detect this type of arrhythmia. There is no classifier to give the best results for all matters at any time and combining classifiers improve the combined system results in comparison with each of the techniques.
In this study, the MIT-BIH arrhythmia database is used as a data source. Two datasets are used for training; the first contains 2400 samples, as in other studies, and the second contains 600 samples, including normal and PVC beats. Morphological features and features obtained from wavelet transform used in a combined classifier were used afterwards, which is the combination of the most common classifiers namely artificial neural network, SVM and KNN for PVC beat classification. Statistical significance features were selected using the p-value approach and normalized them. The best results were obtained when combining all three classifiers and using normalized statistical significance features. The designed hybrid system succeeded to detect PVC beats with 98.9±0.2% accuracy, 99.0±0.1% sensitivity, and 98.8±0.2% specificity. Also, the efficiency of the proposed method was shown when using limited training samples. The results showed the success of the proposed approach, specifically in comparison with other related research studies.
 

Full-Text [PDF 5338 kb]   (2193 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2017/01/20 | Accepted: 2017/10/25 | Published: 2018/06/13 | ePublished: 2018/06/13

References
1. [1] Falik R. "Cardiology Essentials in Clinical Practice", JAMA, Vol. 306, no. 19, pp. 2162-3, Nov. 2011. [DOI:10.1001/jama.2011.1683]
2. [2] Thaler MS. The only EKG book you'll ever need, Lippincott Williams & Wilkins, 2010.
3. [3] Clifford GD, Azuaje F, McSharry P. "Advanced methods and tools for ECG data analysis", Artech House, Inc. Sep. 2006.
4. [4] Sameni R, Shamsollahi MB, Jutten C, Clifford GD. "A nonlinear Bayesian filtering frame-work for ECG denoising", IEEE Trans. Biom-ed. Eng., Vol. 54, no. 12, pp. 2172-85, Dec. 2007. [DOI:10.1109/TBME.2007.897817] [PMID]
5. [5] Sameni R, Shamsollahi MB, Jutten C. "Model-based Bayesian filtering of cardiac contam-inants from biomedical recordings", Physiolo-gical Measurement, Vol. 29, no. 5, pp. 595, May. 2008.
6. [6] McSharry PE, Clifford GD, Tarassenko L, Smith LA. "A dynamical model for generating synthetic electrocardiogram signals", IEEE Trans. Biomed. Eng., Vol. 50, no. 3, pp. 289-94, Mar. 2003. [DOI:10.1109/TBME.2003.808805] [PMID]
7. [7] Clifford GD, Shoeb A, McSharry PE, Janz BA. "Model-based filtering, compression and classification of the ECG", International Journal of Bioelectromagnetism, Vol. 7, no. 1, pp. 158-61, May. 2005.
8. [8] Sayadi O, Sameni R, Shamsollahi MB. "ECG denoising using parameters of ECG dynamical model as the states of an extended Kalman filter". In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, pp. 2548-2551, IEEE, Aug. 2007. [DOI:10.1109/IEMBS.2007.4352848]
9. [9] Sayadi O, Shamsollahi MB. "ECG denoising and compression using a modified extended Kalman filter structure", IEEE Trans. Biomed. Eng., Vol. 55, no. 9 pp. 2240-8, Sep. 2008. [DOI:10.1109/TBME.2008.921150] [PMID]
10. [10] Sayadi O, Shamsollahi MB. "A model-based Bayesian framework for ECG beat segmenta-tion", Physiological measurement, Vol. 30, no.3, pp. 335, Feb. 2009. [DOI:10.1088/0967-3334/30/3/008] [PMID]
11. [11] Ghorbanian P, Ghaffari A, Jalali A, Nataraj C. "Heart arrhythmia detection using continuous wavelet transform and principal component analysis with neural network classifier", IEEE In Computing in Cardiology, pp. 669-672, Sep. 2010.
12. [12] Inan OT, Giovangrandi L, Kovacs GT. "Robust neural-network-based classification of prema-ture ventricular contractions using wavelet transform and timing interval feature-es", IEEE Trans. Biomed. Eng., Vol. 53, no. 12, pp. 2507-15, Dec. 2006. [DOI:10.1109/TBME.2006.880879] [PMID]
13. [13] Melgani F, Bazi Y. "Classification of electrocardiogram signals with support vector machines and particle swarm optimization", IEEE Trans. Information Technology in Biomedicine, Vol. 12, no. 5, pp. 667-77, Sep. 2008. [DOI:10.1109/TITB.2008.923147] [PMID]
14. [14] Sayadi O, Shamsollahi MB, Clifford GD. "Robust detection of premature ventricular contractions using a wave-based Bayesian framework", IEEE Trans. Biomed. Eng., Vol. 57, no. 2, pp. 353-62, Feb. 2010. [DOI:10.1109/TBME.2009.2031243] [PMID] [PMCID]
15. [15] J. A. Gutiérrez-Gnecchi, R. Morfin-Maga˜na, D. Lorias-Espinoza, A. C. Tellez-Anguiano, E. Reyes-Archundia, A. Méndez-Pati˜no, R. Casta˜neda-Miranda, " DSP-based arrhythmia classification using wavelet transform and probabilistic neural network", Biomedical Signal Processing and Control, vol. 32, pp. 44-56, Feb. 2017. [DOI:10.1016/j.bspc.2016.10.005]
16. [16] Christov I, Bortolan G. "Ranking of pattern recognition parameters for premature ventri-cular contractions classification by neural networks", Physiological Measurem-ent, Vol. 25, no. 5, pp. 1281, Aug. 2004. [DOI:10.1088/0967-3334/25/5/017] [PMID]
17. [17] Bortolan G, Jekova I, Christov I. "Comparison of four methods for premature ventricular contraction and normal beat clustering", In Computers in Cardiology, pp. 921-924, IEEE. Sep. 2005.
18. [18] Ince T, Kiranyaz S, Gabbouj M. "Automated patient-specific classification of premature ventricular contractions", In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE., pp. 5474-5477, Aug. 2008. [DOI:10.1109/IEMBS.2008.4650453]
19. [19] Zhou J. "Automatic detection of premature ventricular contraction using quantum neural networks", In Bioinformatics and Bioen-gineering, 2003. Proc. Third IEEE Sympo-sium, pp. 169-173, Mar. 2003.
20. [20] Osowski S, Linh TH. "ECG beat recognition using fuzzy hybrid neural network", IEEE Trans. Biomed. Eng., Vol. 48, no. 11, pp. 1265-71, Nov. 2001. [DOI:10.1109/10.959322] [PMID]
21. [21] Pasolli E, Melgani F. "Active learning methods for electrocardiographic signal classification", IEEE Trans. Information Technology in Biomedicine, Vol. 14, no. 6, pp.1405-16, Nov. 2010. [DOI:10.1109/TITB.2010.2048922] [PMID]
22. [22] Alajlan N, Bazi Y, Melgani F, Malek S, Bencherif MA. "Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods", Signal, Image and Video Processing, Vol. 8, no. 5, pp. 931-42, Jul. 2014. [DOI:10.1007/s11760-012-0339-8]
23. [23] R. Zarei, J. He, G. Huang, Y. Zhang, "Effective and efficient detection of premature ventricular contractions based on variation of principal directions", Digital Signal Proces-sing, vol. 50, pp. 93-102, Mar. 2016. [DOI:10.1016/j.dsp.2015.12.002]
24. [24] I. Kaur, R. Rajni, A. Marwaha, "ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform", J. Inst. Eng. India Ser. B., Vol. 97, no. 4, pp. 499-507, Dec. 2016. [DOI:10.1007/s40031-016-0247-3]
25. [25] The MIT-BIH Arrhythmia Database. (2015, Oct. 8). [Online]. Available: http://physi-onet.org/physiobank/database/mitdb/
26. [26] L. I. Kuncheva, J. C. Bezdek, R. P. W. Duin, "Decision Templates for Multiple Classifier Fusion: An Experimental Comparison," Patt-ern Recognition, vol. 34, no. 2, pp. 299-314, 2001. [DOI:10.1016/S0031-3203(99)00223-X]
27. [27] Huang YS, Suen CY. "The behavior-knowledge space method for combination of multiple classifiers", In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp. 347-347, Jun. 1993. [DOI:10.1109/CVPR.1993.1626170]
28. [28] M.A. Bagheri, Gh. Montazer, and E. Kabir, "A Subspace Approach to Error- Correcting Output Coding", Pattern Recognition Letters, vol. 34, pp. 176–184, 2013 [DOI:10.1016/j.patrec.2012.09.010]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2015 All Rights Reserved | Signal and Data Processing