PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

A new approach to adaptive threshold based method for QRS detection with fuzzy clustering

Autorzy
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The most crucial requirements for a QRS complex detection algorithm are accuracy, precision and repeatability. Most methods of detecting QRS complexes use the approach based on exceeding a certain amplitude threshold. However, the presence of noise in the electro-cardiographic signal can inhibit the accuracy and precision of detection especially for low amplitude QRS-complexes. The proposed algorithm uses a new approach for the amplitude threshold determination and in the decision stage. The fuzzy c-median clustering method is used to determine the amplitude threshold values for each sliding window across the composed detection function waveform. It allows us to adjust threshold value to noise variations in the ECG signal. When a specified amplitude threshold is exceeded by the detection function and finding the peaks in its waveform, potential QRS complexes can be identified. Then the identified peaks are evaluated on the basis of the speed of rising and falling slopes of detection function peak. It enables identification of only those peaks of the detection function whose location corresponds to QRS complexes. ECG recordings taken from the standard-available eight databases are used to evaluate the performance quality of the proposed method. The proposed QRS detector achieved sensitivity of 99.82%, positive predictivity of 99.88% over the validation MIT-BIH Arrhythmia Database. The overall sensitivity and positive predictivity are respectively 99.81% and 99.67%. Advantages of the proposed method are the robustness against noise, the accuracy and the simplicity of the algorithm that evaluates the candidate peaks of the detection function which indicate the QRS complexes.
Twórcy
  • Department of Cybernetics, Nanotechnology and Data Processing, Faculty of Automatic Control, Electronics and Computer Science, 10 Silesian University of Technology, 16 Akademicka Str., 44-100 Gliwice, Poland
Bibliografia
  • [1] World Health Organization (2007). https://www.who. int/cardiovascular_diseases/guidelines/Full.
  • [2] Alwan A. Global status report on noncommunicable diseases 2010 (2011), WHO Press, World Health Organization, 20 Av. Appia, 1211 Geneva 27, Switzerland.
  • [3] Elgendi M. Fast QRS detection with an optimized knowledge-based method: Evaluation on 11 standard ECG databases. PLOS ONE 2013;8(9):1–18. https://doi.org/10.1371/journal. Pone.0073557.
  • [4] Hernandez-Matamoros A, Fujita H, Escamilla-Hernandez E, Perez-Meana H, Nakano-Miyatake M. Recognition of ECG signals using wavelet based on atomic functions. Biocybern Biomed Eng 2020;40:803–14. https://doi.org/10.1016/j. Bbe.2020.02.007.
  • [5] Chen H, Maharatna K. An automatic R and T peak detection method based on the combination of hierarchical clustering and discrete wavelet transform. IEEE J Biomed Health Inform 2020;24(10):2825–32. https://doi.org/10.1109/ JBHI.2020.2973982.
  • [6] Jin L, Dong J. Intelligent health vessel ABC-DE: an electrocardiogram cloud computing service. IEEE Trans Cloud Comput 2020;8(3):861–74. https://doi.org/10.1109/ TCC.2018.2825390.
  • [7] Yakut Ö., Bolat ED. An improved QRS complex detection method having low computational load. Biomed Signal Process Control 2018;42:230–41. https://doi.org/10.1016/j. Bspc.2018.02.004.
  • [8] Hossain MB, Bashar SK, Walkey AJ, McManus DD, Chon KH. An accurate QRS complex and P wave detection in ECG signals using complete ensemble empirical mode decomposition with adaptive noise approach. IEEE Access 2019;7:128869–80. https://doi.org/10.1109/ ACCESS.2019.2939943.
  • [9] Rahul J, Sora M, Sharma LD, Bohat VK. An improved cardiac arrhythmia classification using an RR interval-based approach. Biocybern Biomed Eng 2021;41(2):656–66. https:// doi.org/10.1016/j.bbe.2021.04.004.
  • [10] Singh R, Rajpal N, Mehta R. An empiric analysis of wavelet-based feature extraction on deep learning and machine learning algorithms for arrhythmia classification. Int J Interactive Multimedia Artif Intell 2021;6:25–34. https://doi. org/10.9781/ijimai.2020.11.005.
  • [11] Singh R, Rajpal N, Mehta R. Application-specific discriminant analysis of cardiac anomalies using shift-invariant wavelet transform. Int J E-Health Med Commun 2021;12(4). https:// doi.org/10.4018/IJEHMC.20210701.oa5.
  • [12] Czabański R, Horoba K, Wróbel J, Matonia A, Martinek R, Kupka T, Jeżewski M, Kahankova R, Jeżewski J, Łeski JM. Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine. Sensors 2020;20(3):765. https://doi.org/10.3390/s20030765.
  • [13] Jenkal W, Latif R, Toumanari A, Dliou A, B’charri OE, Maoulainine FM. An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform. Biocybern Biomed Eng 2016;36:499–508. https://doi.org/10.1016/j.bbe.2016.04.001.
  • [14] Mourad N. ECG denoising based on successive local filtering. Biomed Signal Process Control 2022:73–103431. https://doi. org/10.1016/j.bspc.2021.103431.
  • [15] Moeyersons J, Smets E, Morales JF, Gómez AV, Raedt WD, Testelmans D, Buyse B, Hoof CV, Willems R, Huffel SV, Varon C. Artefact detection and quality assessment of ambulatory ECG signals. Comput Methods Programs Biomed 2019;182. https://doi.org/10.1016/j.cmpb.2019.105050 105050.
  • [16] Mishra A, Sahu SS, Sharma R, Mishra SK. Denoising of electrocardiogram signal using S-transform based time-frequency filtering approach. Arab J Sci Eng 2021;46:9515–25. https://doi.org/10.1007/s13369-021-05333-z.
  • [17] Manjunatha BR, Sneha MR. ECG denoising using Wiener filter and Kalman filter. Procedia Comput Sci 2020;171:273–81. https://doi.org/10.1016/j.procs.2020.04.029.
  • [18] Panigrahy D, Sahu PK. Extended Kalman smoother with differential evolution technique for denoising of ECG signal. Austr Phys Eng Sci Med 2016;39:783–95. https://doi.org/ 10.1007/s13246-016-0468-4.
  • [19] Wang G, Yang L, Liu M, Yuan X, Xiong P, Lin F, Liu X. ECG signal denoising based on deep factor analysis. Biomed Signal Process Control 2020;57. https://doi.org/10.1016/j. Bspc.2019.101824 101824.
  • [20] Hesar HD, Mohebbi M. ECG denoising using marginalized particle extended Kalman filter with an automatic particle weighting strategy. IEEE J Biomed Health Inform 2017;21:635–44. https://doi.org/10.1109/JBHI.2016.2582340.
  • [21] Bing P, Liu W, Wang ZY, Zhang Z. Noise Reduction in ECG Signal Using an Effective Hybrid Scheme. IEEE Access 2020;8:160790–801. https://doi.org/10.1109/ ACCESS.2020.3021068.
  • [22] Gutiérrez-Rivas R, García JJ, Marnane WP, Hernández A. Novel real-time low-complexity QRS complex detector based on adaptive thresholding. IEEE Sens J 2015;15(10):6036–43. https://doi.org/10.1109/JSEN.2015.2450773.
  • [23] Mukhopadhyay SK, Krishnan S. Robust identification of QRS-complexes in electrocardiogram signals using a combination of interval and trigonometric threshold values. Biomed Signal Process Control 2020;61. https://doi.org/10.1016/j. Bspc.2020.102007 102007.
  • [24] Rahul J, Sora M, Sharma L. Exploratory data analysis based efficient QRS-complex detection technique with minimal computational load. Phys Eng Sci Med 2020:1–19. https://doi. org/10.1007/s13246-020-00906-y.
  • [25] Malik J, Soliman EZ, Wu HT. An adaptive QRS detection algorithm for ultra-long-term ECG recordings. J Electrocardiol 2020;60:165–71. https://doi.org/10.1016/ j.jelectrocard.2020.02.016.
  • [26] Rahul J, Sora M, Sharma LD. A novel and lightweight P, QRS, and T peaks detector using adaptive thresholding and template waveform. Comput Biol Med 2021;132. https://doi. org/10.1016/j.compbiomed.2021.104307 104307.
  • [27] Rahul J, Sora M, Sharma LD. Dynamic thresholding based efficient QRS complex detection with low computational overhead. Biomed Signal Process Control 2021;67. https://doi. org/10.1016/j.bspc.2021.102519 102519.
  • [28] Khamis H, Weiss R, Xie Y, Chang C, Lovell NH, Redmond SJ. QRS detection algorithm for telehealth electrocardiogram recordings. IEEE Trans Biomed Eng 2016;63(7):1377–88. https://doi.org/10.1109/TBME.2016.2549060.
  • [29] Kumar A, Ranganatham R, Komaragiri R, Kumar M. Efficient QRS complex detection algorithm based on fast Fourier transform. Biomed Eng Lett 2019;9(1):145–51. https://doi.org/ 10.1007/s13534-018-0087-y.
  • [30] Bachi L, Billeci L, Varanini M. QRS detection based on medical knowledge and cascades of moving average filters. Appl Sci 2021;11(15):6995. https://doi.org/10.3390/app11156995.
  • [31] Zalabarria U, Irigoyen E, Martínez R, Lowe A. Online robust R-peaks detection in noisy electrocardiograms using a novel iterative smart processing algorithm. Appl Math Comput 2020;369 . https://doi.org/10.1016/j.amc.2019.124839 124839.
  • [32] Jia M, Li F, Wu J, Chen Z, Pu Y. Robust QRS detection using high-resolution wavelet packet decomposition and time-attention convolutional neural network. IEEE Access 2020;8:16979–88. https://doi.org/10.1109/ ACCESS.2020.2967775.
  • [33] Sharma A, Patidar S, Upadhyay A, Acharya UR. Accurate tunable-Q wavelet transform based method for QRS complex detection. Comput Electr Eng 2019;75:101–11. https://doi.org/ 10.1016/j.compeleceng.2019.01.025.
  • [34] Mourad K, Fethi BR. Efficient automatic detection of QRS complexes in ECG signal based on reverse biorthogonal wavelet decomposition and nonlinear filtering. Measurement 2016;94:663–70. https://doi.org/10.1016/j. Measurement.2016.09.014.
  • [35] Fotoohinasab A, Hocking T, Afghah F. A greedy graph search algorithm based on changepoint analysis for automatic QRS complex detection. Comput Biol Med 2021;130. https://doi. org/10.1016/j.compbiomed.2021.104208 104208.
  • [36] Hopenfeld B. Multiple channel electrocardiogram QRS detection by temporal pattern search. bioRxiv 2021. https:// doi.org/10.1101/2021.08.15.456413.
  • [37] Akhbari M, Ghahjaverestan NM, Shamsollahi MB, Jutten C. ECG fiducial point extraction using switching Kalman filter. Comput Methods Programs Biomed 2018;157:129–36. https:// doi.org/10.1016/j.cmpb.2018.01.018.
  • [38] Xiang Y, Lin Z, Meng J. Automatic QRS complex detection using two-level convolutional neural network. BioMedical Eng OnLine 2018;17. https://doi.org/10.1186/s12938-018-0441-4.
  • [39] Hou Z, Dong Y, Xiang J, Li X. Yang B A real-time QRS detection method based on phase portraits and box-scoring calculation. IEEE Sens J 2018;18(9):3694–702. https://doi.org/ 10.1109/JSEN.2018.2812792.
  • [40] Malleswari P, Bindu C, Prasad K. A hybrid EMD-DWT based algorithm for detection of QRS complex in electrocardiogram signal. J Ambient Intell Humanized Comput 2021;5:1–9. https://doi.org/10.1007/s12652-021-03268-9.
  • [41] Bajaj A, Kumar S. QRS complex detection using fractional Stockwell transform and fractional Stockwell Shannon energy. Biomed Signal Process Control 2019;54. https://doi. org/10.1016/j.bspc.2019.101628 101628.
  • [42] Beyramienanlou H, Lotfivand N. An efficient Teager energy operator-based automated QRS complex detection. J Healthcare Eng 2018;2018. https://doi.org/10.1155/2018/ 8360475.
  • [43] Beyramienanlou H. A robust method to reliable cardiac QRS complex detection based on Shannon energy and Teager energy operator. Circuits Syst Signal Process 2021;40 (2):980–92. https://doi.org/10.1007/s00034-020-01510-x.
  • [44] Everitt BS, Landau S, Leese M, Stahl D. Cluster Analysis, 5th ed. Wiley Series in Probability and Statistics 2011.
  • [45] Rui X, Wunsch D. Survey of clustering algorithms. IEEE Trans Neural Networks 2005;16(3):645–78. https://doi.org/10.1109/TNN.2005.845141.
  • [46] Geweniger T, Zühlke D, Hammer B, Villmann T. Median fuzzy c-means for clustering dissimilarity data. Neurocomputing 2010;73:1109–16. https://doi.org/10.1016/j.neucom.2009.11.020.
  • [47] Bezdek JC. Pattern Recognition with Fuzzy Objective Function Algorithms, Springer; 1981. doi:10.1007/978-1-4757-0450-1.
  • [48] Łeski, J.M. Fuzzy c-ordered-means clustering, Fuzzy Sets and Systems 2016; 286:114–133. https://doi.org/10.1016/j.fss.2014.12.007.
  • [49] Kersten PR. Fuzzy order statistics and their application to fuzzy clustering. IEEE Trans Fuzzy Syst 1999;7(6):708–12. https://doi.org/10.1109/91.811239.
  • [50] Goldberger AL, Amaral LA, Glass L, Hausdorff JM, et al. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 2000;101(23):E215–20. https://doi.org/10.1161/ 01.cir.101.23.e215.
  • [51] Gutiérrez-Rivas R, García JJ, Marnane WP, Hernández Á. Novel real-time low-complexity QRS complex detector based on adaptive thresholding. IEEE Sens J 2015;15(10):6036–43. https://doi.org/10.1109/JSEN.2015.2450773.
  • [52] Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng Med Biol Mag 2001;20(3):45–50. https://doi. Org/10.1109/51.932724.
  • [53] Moody GB, Muldrow WE, Mark RG. A noise stress test for arrhythmia detectors. Comput Cardiol 1984;11:381–4.
  • [54] Iyengar N, Peng C, Morin R, Goldberger A, Lipsitz L. Agerelated alterations in the fractal scaling of cardiac interbeat interval dynamics. Am J Physiol 1996;271:1078–84. https://doi. org/10.1152/ajpregu.1996.271.4.R1078.
  • [55] Laguna P, Mark RG, Goldberg A, Moody GB. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput Cardiol 1997:673–6. https://doi.org/10.1109/CIC.1997.648140.
  • [56] Taddei A, Distante G, Emdin M, Pisani P, Moody GB, Zeelenberg C, Marchesi C. The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. Eur Heart J 1992;13:1164–72. https://doi.org/10.1093/oxfordjournals.eurheartj.a060332.
  • [57] Katsigiannis S, Ramzan N. DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices. IEEE J Biomed Health Inform 2017;22(1):98–107. https://doi.org/10.1109/JBHI.2017.2688239.
  • [58] AAMI EC57:2012 Testing and reporting performance results of cardiac rhythm and ST-segment measurement algorithms, AAMI 2012.https://www.amazon.com/AAMI-EC57- Performance-Measurement-Algorithms/dp/1570204780.
  • [59] Moody G, Moody B, Silva I. Robust Detection of Heart Beats in Multimodal Data - The PhysioNet Computing in Cardiology Challenge 2014, https://physionet.org/content/challenge2014/1.0.0/, [Online; accessed 19-May-2021].
  • [60] Mourad K, Fethi BR. Efficient automatic detection of QRS complexes in ECG signal based on reverse biorthogonal wavelet decomposition and nonlinear filtering. Measurement 2016;94:663–70. https://doi.org/10.1016/j. Measurement.2016.09.014.
  • [61] Pander T. EEG signal improvement with cascaded filter based on OWA operator. Signal Image Video Process 2019;13 (6):1165–71. https://doi.org/10.1007/s11760-019-01458-9.
  • [62] Goldberger A. Clinical electrocardiography: A simplified approach. 8th ed, 2012.
  • [63] Afonso V, Tompkins W. Detecting Ventricular-Fibrillation – Selecting The Appropriate Time-Frequency Analysis Tool For the Application. IEEE Eng Med Biol Mag 1995;14(2):152–9. https://doi.org/10.1109/51.376752.
  • [64] Ibtehaz N, Rahman MS, Rahman MS. VFPred: A fusion of signal processing and machine learning techniques in detecting ventricular fibrillation from ECG signals. Biomed Signal Process Control 2019;49:349–59. https://doi.org/ 10.1016/j.bspc.2018.12.016.
  • [65] Yasumura K, Patel NJ, Vengrenyuk Y, Kini AS, Baber U. Ventricular Fibrillation Associated With Coronary Plaque Erosion Detected by Optical Coherence Tomography. JACCCardiovascular Interventions 2020;13(1):E5–7. https://doi.org/10.1016/j.jcin.2019.08.050.
  • [66] Sejdić E, Djurović I, Jiang J. Time-frequency feature representation using energy concentration: An overview of recent advances. Digital Signal Process 2009;19(1):153–83. https://doi.org/10.1016/j.dsp.2007.12.004.
  • [67] Pander T, Czabański R, Przybyła T, Pojda-Wilczek D. An automatic saccadic eye movement detection in an optokinetic nystagmus signal. Biomed Eng/Biomedizinische Technik 2014;59:529–43. https://doi.org/10.1515/bmt-2013-0137.
  • [68] Pander T, Przybyła T, Fuzzy-based algorithm for QRS detection, in: K.T. Atanassov, V. Atanassova, J. Kacprzyk, A. Kałuszko, M. Krawczak, J.W. Owsiński, S.S. Sotirov, E. Sotirova, E. Szmidt, S. Zadrozny (Eds.), Advances and New _ Developments in Fuzzy Logic and Technology, Springer International Publishing, 2021, 202–215. doi:10.1007/978-3-030-77716-6_19.
  • [69] Farashi S. A multiresolution time-dependent entropy method for QRS complex detection. Biomed Signal Process Control 2016;24:63–71. https://doi.org/10.1016/j.bspc.2015.09.008.
  • [70] Zhang Z, Yu Q, Zhang Q, Ning N, Li J. A Kalman filtering based adaptive threshold algorithm for QRS complex detection. Biomed Signal Process Control 2020;58. https://doi.org/ 10.1016/j.bspc.2019.101827 101827.
  • [71] Xiong H, Liang M, Liu J. A real-time QRS detection algorithm based on energy segmentation for exercise electrocardiogram. Circuits Syst Signal Process 2021;40 (10):4969–85. https://doi.org/10.1007/s00034-021-01702-z.
  • [72] Gupta V, Mittal M. Efficient R-peak detection in electrocardiogram signal based on features extracted using Hilbert transform and Burg method. J Inst Eng (India): Series B 2020;101:23–34. https://doi.org/10.1007/s40031-020-00423-2.
  • [73] Chandra BS, Sastry CS, Jana S. Robust heartbeat detection from multimodal data via CNN-based generalizable information fusion. IEEE Trans Biomed Eng 2019;66(3):710–7. https://doi.org/10.1109/TBME.2018.2854899.
  • [74] Chen A, Zhang Y, Zhang M, Liu W, Chang S, Wang H, He J, Huang Q. A real time QRS detection algorithm based on ET and PD controlled threshold strategy. Sensors 2020;20 (14):4003. https://doi.org/10.3390/s20144003.
  • [75] Dohare AK, Kumar V, Kumar R. An efficient new method for the detection of QRS in electrocardiogram. Comput Electr Eng 2014;40(5):1717–30. https://doi.org/10.1016/ j.compeleceng.2013.11.004.
  • [76] Qaisar SM, Fesquet L, Renaudin M. An adaptive resolution computationally efficient short-time Fourier transform. J Electr Comput Eng 2008;2008. https://doi.org/10.1155/2008/ 932068.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-07d32e45-2f81-4c27-af71-90b1500b1269
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.