PVC arrhythmia classification based on fractional order system modeling
-
Imen Assadi
,
Abdelfatah Charef
Abstract
It is well known that many physiological phenomena are modeled accurately and effectively using fractional operators and systems. This type of modeling is due mainly to the dynamical link between fractional-order systems and the fractal structures of the physiological systems. The automatic characterization of the premature ventricular contraction (PVC) is very important for early diagnosis of patients with different life-threatening cardiac diseases. In this paper, a classification scheme of normal and PVC beats of the electrocardiogram (ECG) signal is proposed. The clustering features used for normal and PVC beats discrimination are the parameters of the commensurate order linear fractional model of the frequency content of the QRS complex of the ECG signal. A series of tests and comparisons have been performed to evaluate and validate the efficiency of the proposed PVC classification algorithm using the MIT-BIH arrhythmia database. The proposed PVC classification method has achieved an overall accuracy of 94.745%, a specificity of 95.178% and a sensitivity of 90.021% using all the 48 records of the database.
Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interest: The authors declare no conflicts of interest.
Statement of human studies: No human studies were carried out by the authors for this article.
Animal studies: No animals were used in this study.
References
1. Sharma, T, Sharma, K. A new method for QRS detection in ECG signals using QRS-preserving filtering techniques. Biomed Eng-Biomed Tech 2017;63:207–17.10.1515/bmt-2016-0072Suche in Google Scholar PubMed
2. Ham, FM, Han, S. Classification of cardiac arrhythmias using fuzzy ARTMAP. IEEE Trans Biomed Eng 1996;43:25–430. https://doi.org/10.1109/10.486263.Suche in Google Scholar PubMed
3. Kandala, R, Dhuli, R, Pławiak, P, Naik, G, Moeinzadeh, H, Gargiulo, G, et al.. Towards real-time heartbeat classification: evaluation of nonlinear morphological features and voting method. Sensors 2019;19:1–27. https://doi.org/10.3390/s19235079.Suche in Google Scholar PubMed PubMed Central
4. Zhan, X, Zhang, L, Wang, K, Yu, C, Zhu, T, Tang, J. A rapid approach to assess cardiac contractility by ballistocardiogram and electrocardiogram. Biomed Eng-Biomed Tech 2016;63:113–22.10.1515/bmt-2015-0204Suche in Google Scholar PubMed
5. Pławiak, P, Acharya, UR. Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals. Neural Comput Appl 2020;32:11137–61.10.1007/s00521-018-03980-2Suche in Google Scholar
6. Assadi, I, Charef, A, Bensouici, T, Belgacem, N. Arrhythmias discrimination based on fractional order system and KNN classifier. In: Proceedings of the 2nd IET international conference on intelligent signal processing ISP. IET London: Savoy Place, UK; 2015.10.1049/cp.2015.1781Suche in Google Scholar
7. Balouchestani, M, Krishnan, S. Advanced K-means clustering algorithm for large ECG data sets based on a collaboration of compressed sensing theory and K-SVD approach. Signal Image Video Process 2016;10:113–20. https://doi.org/10.1007/s11760-014-0709-5.Suche in Google Scholar
8. Daamouche, A, Hamami, L, Alajlan, N, Melgani, F. A wavelet optimization approach for ECG signal classification. Biomed Signal Process Contr 2012;7:342–9. https://doi.org/10.1016/j.bspc.2011.07.001.Suche in Google Scholar
9. Reulecke, S, Charleston-Villalobos, S, Voss, A, González-Camarena, R, González-Hermosillo, J, Gaitán-González, M, et al.. Dynamics of the cardiovascular autonomic regulation during orthostatic challenge is more relaxed in women. Biomed Eng-Biomed Tech 2018;63:139–50.10.1515/bmt-2016-0150Suche in Google Scholar PubMed
10. Alarsan, FI, Younes, M. Analysis and classification of heart diseases using heartbeat features and machine learning algorithms. J Big Data 2019;6:1–15. https://doi.org/10.1186/s40537-019-0244-x.Suche in Google Scholar
11. Pławiak, P, Abdar, M. Novel methodology for cardiac arrhythmias classification based on long-duration ECG signal fragments analysis. In: Ganesh, N, editor. Biomedical signal processing – advances in theory, algorithms, and applications. Singapore: Springer; 2020:225–72 pp.10.1007/978-981-13-9097-5_11Suche in Google Scholar
12. Tuncer, T, Dogan, S, Pławiak, P, Rajendra, AU. Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowl Base Syst 2019:186. https://doi.org/10.1016/j.knosys.2019.104923.Suche in Google Scholar
13. Thomas, M, Kr-Das, M, Ari, S. Automatic ECG arrhythmia classification using dual tree complex wavelet based features. AEU-Int J Electron Commun 2015;69:715–21. https://doi.org/10.1016/j.aeue.2014.12.013.Suche in Google Scholar
14. De-Chazel, P, Reilly, R. A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 2006;53:2535–43.10.1109/TBME.2006.883802Suche in Google Scholar PubMed
15. Mazidi, MH, Eshghi, M, Raoufy, MR. Detection of premature ventricular contraction (PVC) using linear and nonlinear techniques: an experimental study. Cluster Comput 2020;186:759–74. https://doi.org/10.1007/s10586-019-02953-x.Suche in Google Scholar
16. Jung, Y, Kim, H. Detection of PVC by using a wavelet-based statistical ECG monitoring procedure. Biomed Signal Process Contr 2017;36:176–82. https://doi.org/10.1016/j.bspc.2017.03.023.Suche in Google Scholar
17. Christov, I, Jekova, I, Bortolan, G. Premature ventricular contraction classification by the Kth nearest-neighbours rule. Physiol Meas 2005;26:123–30. https://doi.org/10.1088/0967-3334/26/1/011.Suche in Google Scholar PubMed
18. Khazaee, A. Combining SVM and PSO for PVC detection. Int J Adv Eng Sci 2013;3:1–5.Suche in Google Scholar
19. Malek, AS, Elnahrawy, A, Anwar, H, Naeem, M. Automated detection of premature ventricular contraction in ECG signals using enhanced template matching algorithm. Biomed Phys Eng Express 2020;6:15–24. https://doi.org/10.1088/2057-1976/ab6995.Suche in Google Scholar PubMed
20. Inan, O, Giovangrandi, L, Kavacs, G. Robust neural-network based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Trans Biomed Eng 2006;53:2507–15. https://doi.org/10.1109/tbme.2006.880879.Suche in Google Scholar
21. Kaya, Y, Pehlivan, H. Classification of premature ventricular contraction in ECG. Int J Adv Comput Sci Appl 2015;6:34–40. https://doi.org/10.14569/IJACSA.2015.060706.Suche in Google Scholar
22. Hou, B, Yang, J, Wang, P, Yan, R. LSTM-based auto-encoder model for ECG arrhythmias classification. IEEE Trans Instrum Meas 2019;69:1232–40. https://doi.org/10.1109/tim.2019.2910342.Suche in Google Scholar
23. Talbi, ML, Ravier, P. Detection of PVC in ECG signals using fractional linear prediction. Biomed Signal Process Contr 2016;23:42–51. https://doi.org/10.1016/j.bspc.2015.07.005.Suche in Google Scholar
24. Talbi, ML, Charef, A. PVC discrimination using the QRS power spectrum and self-organizing maps. Comput Methods Progr Biomed 2009;94:223–31. https://doi.org/10.1016/j.cmpb.2008.12.009.Suche in Google Scholar PubMed
25. Melgani, F, Bazi, Y. Detecting premature ventricular contractions in ECG signals with Gaussian processes. Comput Cardiol 2008;35:237–40.10.1109/CIC.2008.4749021Suche in Google Scholar
26. Sayadi, O, Shamsollahi, M, Clifford, G. Robust detection of premature ventricular contractions using a wave-based Bayesian framework. IEEE Trans Biomed Eng 2010;57:353–62. https://doi.org/10.1109/tbme.2009.2031243.Suche in Google Scholar PubMed PubMed Central
27. Alajlan, N, Bazi, Y, Melgani, F, Malek, S, Bencherif, M. Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods. Signal Image Video Process 2014;8:931–42. https://doi.org/10.1007/s11760-012-0339-8.Suche in Google Scholar
28. Krasteva, V, Jekova, E. QRS template matching for recognition of ventricular ectopic beats. Ann Biomed Eng 2007;35:2065–76. https://doi.org/10.1007/s10439-007-9368-9.Suche in Google Scholar PubMed
29. Chen, S. A nonlinear trimmed moving averaging-based system with its application to real-time QRS beat classification. J Med Eng Technol 2007;31:443–9. https://doi.org/10.1080/03091900701234267.Suche in Google Scholar PubMed
30. KAYA, Y. Classification of PVC beat in ECG using basic temporal features. Balkan J Electr Comput Eng 2018;6:78–82.10.17694/bajece.419541Suche in Google Scholar
31. Peng Li, P, Liu, C, Wang, X, Zheng, D, Li, Y, Liu, C. A low-complexity data-adaptive approach for premature ventricular contraction recognition. Signal Image Video Process 2014;8:111–20. https://doi.org/10.1007/s11760-013-0478-6.Suche in Google Scholar
32. Hilfer, R. Applications of calculus in physics. Singapore: World Scientific; 2000.10.1142/3779Suche in Google Scholar
33. Machado, JA. And I say to myself: what a fractional world! Fractional Calc Appl Anal 2011;14:635–54.10.2478/s13540-011-0037-1Suche in Google Scholar
34. Magin, R, editor. Fractional calculus in bioengineering. CT, USA: Begell House, Redding; 2006.Suche in Google Scholar
35. Sabatier, J, Agrawal, OP, Machado, JT, editors. Advances in fractional calculus: theoretical development and applications in physics and engineering. The Netherlands: Springer, Dordrecht; 2007.10.1007/978-1-4020-6042-7Suche in Google Scholar
36. Baleanu, D, Guvenc, Z, Machado, J, editors. New trends in nanotechnology and fractional calculus applications. New York, USA: Springer; 2010.10.1007/978-90-481-3293-5Suche in Google Scholar
37. Ionescu, CM, Sabatier, J, Machado, JAT. Fractional signals and systems. Signal Image Video Process 2012;6:341–2. https://doi.org/10.1007/s11760-012-0313-5.Suche in Google Scholar
38. Das, S, Pan, I, editors. Fractional order signal processing: introductory concepts and applications. New York, USA: Springer; 2012.10.1007/978-3-642-23117-9_6Suche in Google Scholar
39. Goldberger, A, Bhargava, V, West, B, Mandel, A. On a mechanism of cardiac electrical stability. The fractal hypothesis. Biophys J 1985;48:525–8. https://doi.org/10.1016/s0006-3495(85)83808-x.Suche in Google Scholar
40. Ferdi, Y, Herbeuval, J, Charef, A, Boucheham, B. R wave detection using fractional digital differentiation. ITBM-RBM 2003;24:273–80. https://doi.org/10.1016/j.rbmret.2003.08.002.Suche in Google Scholar
41. Assadi, I, Charef, A, Copot, D, De-Keyser, R, Bensouici, T, Ionescu, C. Evaluation of respiratory parameters by means of fractional order models. Biomed Signal Process Contr 2017;34:206–13. https://doi.org/10.1016/j.bspc.2017.02.006.Suche in Google Scholar
42. Assadi, I, Charef, A, Bensouici, T, Belgacem, N, Nait-Ali, A. QRS complex based human identification. In: Proceedings of international conference on signal and image processing applications (ICSIPA). Kuala Lumpur, Malaysia: IEEE; 2015. https://doi.org/10.1109/ICSIPA.2015.7412198.Suche in Google Scholar
43. Ionescu, CM. The human respiratory system: an analysis of the interplay between anatomy, structure, breathing and fractal dynamics. London: Springer Science & Business Media; 2013.10.1007/978-1-4471-5388-7Suche in Google Scholar
44. West, B. Fractal physiology and the fractional calculus: a perspective. Front Physiol 2010;1:12. https://doi.org/10.3389/fphys.2010.00012.Suche in Google Scholar PubMed PubMed Central
45. Magin, R, Ortigueira, M, Podlubny, I, Trujillo, J. On the fractional signals and systems. Signal Process 2011;91:350–71. https://doi.org/10.1016/j.sigpro.2010.08.003.Suche in Google Scholar
46. Mark, R, Moody, G. MIT-BIH arrhythmia database. Available from: http://www.physionet.org/physiobank/database/mitdb/ [Accessed 20 Jan 2012].Suche in Google Scholar
47. Sun, H, Charef, A. Fractal systems: a time domain approach. Ann Biomed Eng 1990;18:597–621. https://doi.org/10.1007/bf02368450.Suche in Google Scholar PubMed
48. Benmalek, M, Charef, A, Abdelliche, F. Preprocessing of the ECG signals using the his-Purkinje fractal system. In: Proceedings of the 7th international multi conference on systems, signals and devices, SSD10. Amman, Jordan: IEEE; 2010.10.1109/SSD.2010.5585574Suche in Google Scholar
49. Valério, D. Ninteger v. 2.3 fractional control toolbox for MatLab; 2005. Available from: http://web.ist.utl.pt/∼duarte.valerio/ninteger/ninteger.htm.Suche in Google Scholar
50. Fariha, Z, Apandi, M, Ikeura, R, Hayakawa, S, Tsutsumi, S. An analysis of the effects of noisy electrocardiogram signal on heartbeat detection performance. Bioengineering 2020;7.10.3390/bioengineering7020053Suche in Google Scholar PubMed PubMed Central
51. Peng, CC. A memory-optimized architecture for ECG signal processing [PhD thesis]. Gainsville, USA: University of Florida; 2011.Suche in Google Scholar
52. Dong, J, Zhang, J, Zhu, H, Wang, L, Liu, X, Li, Z. A remote diagnosis service platform for wearable ECG monitors. IEEE Intel Syst 2012;27:36–43. https://doi.org/10.1109/mis.2012.4.Suche in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Attention based convolutional network for automatic sleep stage classification
- Emotion recognition using time–frequency ridges of EEG signals based on multivariate synchrosqueezing transform
- A novel signal to image transformation and feature level fusion for multimodal emotion recognition
- PVC arrhythmia classification based on fractional order system modeling
- A clinical set-up for noninvasive blood pressure monitoring using two photoplethysmograms and based on convolutional neural networks
- Virtual simulation of otolith movement for the diagnosis and treatment of benign paroxysmal positional vertigo
- Development and control of a home-based training device for hand rehabilitation with a spring and cable driven mechanism
- An easy and low-cost biomagnetic methodology to study regional gastrointestinal transit in rats
- Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks
- Comparison of a standardized four-point bending test to an implant system test of an osteosynthetic system under static and dynamic load condition
- An application of finite element method in material selection for dental implant crowns
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Attention based convolutional network for automatic sleep stage classification
- Emotion recognition using time–frequency ridges of EEG signals based on multivariate synchrosqueezing transform
- A novel signal to image transformation and feature level fusion for multimodal emotion recognition
- PVC arrhythmia classification based on fractional order system modeling
- A clinical set-up for noninvasive blood pressure monitoring using two photoplethysmograms and based on convolutional neural networks
- Virtual simulation of otolith movement for the diagnosis and treatment of benign paroxysmal positional vertigo
- Development and control of a home-based training device for hand rehabilitation with a spring and cable driven mechanism
- An easy and low-cost biomagnetic methodology to study regional gastrointestinal transit in rats
- Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks
- Comparison of a standardized four-point bending test to an implant system test of an osteosynthetic system under static and dynamic load condition
- An application of finite element method in material selection for dental implant crowns
