دوره 18، شماره 2 - ( 7-1400 )                   جلد 18 شماره 2 صفحات 176-163 | برگشت به فهرست نسخه ها

XML English Abstract Print


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

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-fa.html
واقفی مهسا، جمشیدی فاطمه. انتخاب ویژگی برای تشخیص آریتمی‌های قلبی با استفاده از بهینه‌سازی ازدحام ذرات دودویی چند‌هدفه. پردازش علائم و داده‌ها. 1400; 18 (2) :176-163

URL: http://jsdp.rcisp.ac.ir/article-1-972-fa.html


گروه مهندسی برق، واحد شیراز، دانشگاه آزاد اسلامی
چکیده:   (358 مشاهده)
سیگنال الکتروکاردیوگرام، یکی از مهم­ترین ابزار برای طبقه­‌بندی انواع مختلف آریتمی­‌های قلبی است. به‌طورمعمول سیگنال­‌های ECG، حاوی نوفه‌های متفاوتی هستند. در این مقاله، تکنیک تجزیه مد تجربی گروهی که در آن هر تابع مد ذاتی (IMF)، شامل تنها یک مؤلفه فرکانسی است، برای حذف نوفه به کار رفته است. با کمک پنجره­‌بندی مناسب بر روی کمپلکس QRS متشکل از جمع سه IMF نخست، حذف نوفه با کمترین اعوجاج انجام شده و با استفاده از تبدیل موجک گسسته، نوفه‌های باقی‌مانده نیز از بین رفته، سپس با به­‌کارگیری تجزیه بسته موجک، از سیگنال ویژگی استخراج شده است که ویژگی‌­های بهینه، با روش بهینه‌سازی ازدحام ذرات دودویی چند‌هدفه انتخاب شده­اند؛ در‌نهایت از شبکه عصبی پس‌انتشار، برای طبقه­‌بندی استفاده شده که مقدار دقت 12/99 درصد برای 17 عدد سیگنال دریافت‌‌شده از پایگاه داده MIT-BIH، به‌دست آمده است.
متن کامل [PDF 1228 kb]   (136 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: مقالات گروه علائم حیاتی ( مرتبط با مهندسی پزشکی)
دریافت: 1397/11/25 | پذیرش: 1399/5/28 | انتشار: 1400/7/16 | انتشار الکترونیک: 1400/7/16

فهرست منابع
1. [1]. گایتون آ، هال ج ا، 1389، "فیزیولوژی پزشکی"، ترجمه شادان ف، تهران، انتشارات چهر، جلد اول.
2. [1] A. Guyton, H. Hall, "Medical Physiology", Translated by F. Shadan, Tehran, Chehr Publication, Vol. I, 2010.
3. [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]
4. [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.
5. [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]
6. [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]
7. [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]
8. [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]
9. [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.
10. [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]
11. [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]
12. [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]
13. [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]
14. [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]
15. [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]
16. [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]
17. [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]
18. [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]
19. [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]
20. [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]
21. [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]
22. [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.
23. [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]
24. [23]. رهبری پور، م. و محمدزاده اصل، ب. 1397. تشخیص آریتمی انقباضات زودرس بطنی در سیگنال الکتریکی قلب با استفاده ازترکیب طبقه بندها. پردازش علایم و داده ها, 15(1), 55-70.
25. [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]
26. [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]
27. [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]
28. [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]
29. [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]
30. [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]
31. [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]
32. [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]
33. [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]
34. [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]
35. [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]
36. [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]
37. [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]
38. [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.
39. [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]

ارسال نظر درباره این مقاله : نام کاربری یا پست الکترونیک شما:
CAPTCHA

ارسال پیام به نویسنده مسئول


بازنشر اطلاعات
Creative Commons License این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است.

کلیه حقوق این تارنما متعلق به فصل‌نامة علمی - پژوهشی پردازش علائم و داده‌ها است.