PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Characterization of cardiac arrhythmias by variational mode decomposition technique

Autorzy
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Automatic detection of cardiac abnormalities in early stage is a popular area of research for decades. In this work a novel algorithm for detection of cardiac arrhythmia is proposed using variational mode decomposition (VMD). Arrhythmia is a crucial abnormality of heart in which the rhythmic disorder may lead to sudden cardiac arrest. Existing algorithms for arrhythmia detection are based on accuracy of detection of fiducial points, parameter selection and extraction, quality of classifier and other factors. Unlike other works, proposed method tries to characterize both atrial and ventricular arrhythmias simultaneously and independently from the segmented sections of the signal. VMD, being able to separate closely spaced frequencies, has a good potential to be useful to provide significant features in transformed domain. Unique feature combinations are also proposed to characterize different arrhythmic events.
Twórcy
autor
  • Department of Applied Electronics and Instrumentation Engineering, Haldia Institute of Technology, Haldia, West Bengal, India
autor
  • Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
autor
  • Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
Bibliografia
  • [1] Li Y, Bisera J, Weil M, Tang W. An algorithm used for ventricular fibrillation detection without interrupting chest compression. IEEE Trans Biomed Eng 2012;59(January (1)):78–86.
  • [2] Luthra A. ECG made easy. New Delhi, India: J.B. Medical Publishers Pvt. Ltd; 1998, 179 pp.
  • [3] Goldman M, editor. Principle of clinical electrocardiography. 11th ed. Los Altos, CA: Lange Medical Publication; 1982.
  • [4] Wagner GS. Marriott's practical electrocardiography. 10th ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2001.
  • [5] Luthra A. ECG made easy. Jaypee Brother Medical Publishers; 2012, 87 pp., ISBN 978-93-5025-591-9.
  • [6] Sajjan M. Learn ECG in a day: a systematic approach. Jaypee Brother Medical Publishers; 2012, 49 pp., ISBN 978-93-5090- 086-4.
  • [7] Shyu L-Y, Wu Y-H, Hu W. Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG. IEEE Trans Biomed Eng 2004;51(July (7)):1269–73.
  • [8] Lim JS. Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system. IEEE Trans Neural Netw 2009;20(March (3)):522–7.
  • [9] Inan OT, Giovangrandi L, Kovacs GTA. Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Trans Biomed Eng 2006;53(December (12)):2507–15.
  • [10] Sayadi O, Shamsollahi MB, Clifford GD. Robust detection of premature ventricular contractions using a wave-based Bayesian framework. IEEE Trans Biomed Eng 2010;57 (February (2)):353–62.
  • [11] Berbari EJ, Bock EA, Cházaro AC, Sun X, Sörnmo L. High-resolution analysis of ambulatory electrocardiograms to detect possible mechanisms of premature ventricular beats. IEEE Trans Biomed Eng 2005;52(April (4)):593–7.
  • [12] Kamath C. ECG beat classification using features extracted from Teager energy functions in time and frequency domains. IET Signal Process 2011;5(6):575–81.
  • [13] Ghorbanian P, Ghaffari A, Jalali1 A, Nataraj C. Heart arrhythmia detection using continuous wavelet transform and principal component analysis with neural network classifier. Comput Cardiol 2010;(37):669–72.
  • [14] Liu S-H, Chang K-M, Wang J-J. An autometic system for ECG arrhythmias classification. IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2011. pp. 2290–4.
  • [15] Homaeinezhad MR, Tavakkoli E, Ghaffari A. Discrete wavelet-based fuzzy network architecture for ECG rhythm- type recognition: feature extraction and clustering-oriented tuning of fuzzy inference system. Int J Signal Process Image Process Pattern Recogn 2011;4(September (3)):107–30.
  • [16] Ceylan R, Özbay Y. Wavelet neural network for classification of bundle branch blocks. WCE; 2011, ISBN: 978-988-19251-4-5.
  • [17] Lin Y-J, Tsai S-N, Yang J-X. Learning ECG patterns with the aid of multilayer perceptrons and classification trees. ICBBE; 2008. p. 1859–62.
  • [18] Pan S-T, Chiou Y-J, Hong T-P, Chen H-C. Automatic recognition for arrhythmias with the assistance of hidden Markov model. ICICS; 2013. p. 1–5.
  • [19] Ebrahimzadeh A, Khazaee A. Higher order statistics for automated classification of ECG beats. ICECE; 2011. p. 5952–5.
  • [20] Y.-C. Yeh, H.-J. Lin, Cardiac arrhythmia diagnosis method using fuzzy C-means algorithm on ECG signals, 3CA-2010, vol. 1, pp. 272–5.
  • [21] Megat Ali MSA, Jahidin AH, Norali AN. Hybrid multilayered perceptron network for classification of bundle branch blocks. ICoBE; 2012. p. 149–54.
  • [22] Lee J, Reyes BA, McManus DD, Mathias O, Chon KH. Atrial fibrillation detection using an iPhone 4S. IEEE Trans Biomed Eng 2013;60(1):203–6.
  • [23] Lee J, McManus D, Chon K. Atrial fibrillation detection using time-varying coherence function and shannon entropy. 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts, USA. 2011. pp. 4685–8.
  • [24] Helfenbein E, Gregg R, Lindauer J, Zhou S. An automated algorithm for the detection of atrial fibrillation in the presence of paced rhythm. Comput Cardiol 2010;37:113–6.
  • [25] Yaghouby F, et al. Towards automatic detection of atrial fibrillation: A hybrid computational approach. Comput Biol Med 2010. http://dx.doi.org/10.1016/j.compbiomed.2010.10.004.
  • [26] Houben RPM, Natasja MS de G, Allessie MA. Analysis of fractionated atrial fibrillation electrograms by wavelet decomposition. IEEE Trans Biomed Eng 2010;57(June (6)).
  • [27] Meo M, Zarzoso V, Meste O, Latcu DG, Saoudi N. Spatial variability of the 12-lead surface ECG as a tool for noninvasive prediction of catheter ablation outcome in persistent atrial fibrillation. IEEE Trans Biomed Eng 2013; 60(January (1)):20–7.
  • [28] Stridh M, Rosenqvist M. Automatic screening of atrial fibrillation in thumb-ECG recordings. Comput Cardiol 2012;39:193–6.
  • [29] Weng W, Blanco-Velasco M, Barner KE. ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Elsevier J Comput Biol Med 2008;38(1):1–13.
  • [30] Sanchez C, Millet J, Rieta JJ, Rodenas J, Castells F, Ruiz R, et al. Packet wavelet decomposition: an approach for atrial activity extraction. IEEE Comput Cardiol 2002;September (29):33–6. Memphis (TN).
  • [31] Lee J, Nam Y, McManus DD, Chon Ki H. Time-varying coherence function for atrial fibrillation detection. IEEE Trans Biomed Eng 2013;60(October (10)).
  • [32] Pal S, Maji U, Mitra M. Characterizing atrial fibrillation in empirical mode decomposition domain. J Med Biol Eng 2016;36(October (5)):693–703. Springer.
  • [33] Giraldo BF, Laguna P, Jane R, Caminal P. Automatic detection of atrial fibrillation and flutter using the differentiated ECG signal. IEEE Comput Cardiol 1995; 369–72.
  • [34] Hoppe BL, Kahn AM, Feld GK, Hassankhani A, Narayan SM. Separating atrial flutter from atrial fibrillation with apparent electrocardiographic organization using dominant and narrow F-wave spectra. J Am Coll Cardiol 2005;46(11):2079–87. Published by Elsevier Inc.
  • [35] ChristovIv I, Bortolan G, Daskalov I. Sequential analysis for automatic detection of atrial fibrillation and flutter. IEEE Comput Cardiol 2001;28:293–6.
  • [36] Dragomiretskiy K, Zosso D. Variational mode decomposition. IEEE Trans Signal Process 2014 Feb;62 (3):531–44.
  • [37] Jenkal W, Latif R, Toumanari A, Dliou A, El B'charri O, Maoulainine FMR. An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform. Biocybern Biomed Eng 2016;36 (3):499–508.
  • [38] Nocedal J, Wright SJ. Numerical optimization. 2nd ed. Berlin, Germany: Springer; 2006.
  • [39] Maji U, Mitra M, Pal S. Detection and characterisation of QRS complex in VMD domain. Michael Faraday IET International Summit. 2015. pp. 586–9.
  • [40] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101(June (23)): e215–20 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215].
  • [41] Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A: Math Phys Eng Sci 1998;454(March (1971)):903–95.
  • [42] Hassan AR. Mohammed Imamul Hassan Bhuiyan, ‘‘Automatic sleep scoring using statistical features in the EMD domain and ensemble methods’’. Biocybern Biomed Eng 2016;36:248–55.
  • [43] Pal S, Mitra M. Empirical mode decomposition based ECG enhancement and QRS detection’’, ELSEVIER. Comput Biol Med 2012;42:83–92.
  • [44] Maji U, Pal S, Majumder S. Estimation of arrhythmia episode using variational mode decomposition technique. IEEE International Instrumentation and Measurement Conference I2MTC. 2015. pp. 767–71.
  • [45] Sayadi O, Shamsollahi MB. Model-based fiducial points extraction for baseline wandered electrocardiograms. IEEE Trans Biomed Eng 2008;55(1):347–51.
  • [46] Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng 1985;32:230–6.
  • [47] Liu X, Zheng Y, Myint WP, Zhao B, Je M, Yuan X. Multiple functional ECG signal is processing for wearable applications of long-term cardiac monitoring. IEEE Trans Biomed Eng 2011;58(February (2)):380–9.
  • [48] Ravanshad N, Rezaee-Dehsorkh H, Lotfi R, Lian Y. A level-crossing based QRS-detection algorithm for wearable ECG sensors. IEEE J Biomed Health Inform 2014;18(January (1)):183–92.
  • [49] Tan X, Chen X, Hu X, Ren R, Zhou B, Fang Z, et al. EMD-based electrocardiogram delineation for a wearable low-power ECG monitoring device. Can J Elect Comput Eng 2014;37(4):212–21.
  • [50] Castro D, Félix P, Presedo J. A method for context-based adaptive QRS clustering in real-time. IEEE J Biomed Health Inform 2015;19(5):1660–71.
  • [51] Yeh Y-C. An analysis of ECG beats by using the mahalanobis distance method. Fourth International Conference on Innovative Computing, Information and Control. 2009. pp. 1460–3.
  • [52] Thong T, Mc Names J, Aboy M, Goldstein B. Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes. IEEE Trans Biomed Eng 2004;51(4):561–9.
  • [53] Brueser C, Diesel J, Zink MDH, Winter S, Schauerte P, Leonhardt S. Automatic detection of atrial fibrillation in cardiac vibration signals. IEEE J Health Inform 2013;17 (1):162–71.
  • [54] Huang C, Ye S, Chen H, Li D, He F, Tu Y. A novel method for detection of the transition between atrial fibrillation and sinus rhythm. IEEE Trans Biomed Eng 2011;58(4):1113–9.
  • [55] Shyu L-Y, Wu Y-H, Hu W. Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG. IEEE Trans Biomed Eng 2004;51(July (7)):1269–73.
  • [56] Chong JW, Esa N, McManus DD, Chon Ki H. Arrhythmia discrimination using a smart phone. IEEE J Biomed Health Informs 2015;19(May (3)):815–24.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c05e7caf-588e-4234-9c96-180413125670
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.