Recognition of ECG signals using wavelet based on atomic functions

• Autorzy: Hernandez-Matamoros Andres , Fujita Hamido , Escamilla-Hernandez Enrique , Perez-Meana Hector , Nakano-Miyatake Mariko

Identyfikatory

DOI

10.1016/j.bbe.2020.02.007

• Języki publikacji: EN

Abstrakty

EN

Heart disease is the principal cause of death across the globe and the ECG signals are used to diagnose it. The correct classification of this disease allows us the opportunity to apply a more focused treatment. ECG signals are fed into Automated Diagnosis Systems, and these systems use techniques like processing digital signals, machine learning, and deep learning. This paper shows the results when the sampling frequency of the ECG signals is resampled and proposes a new preprocessing stage. The new stage applies Wavelet based on Atomic Functions to eliminate the noise and baseline wander. The Wavelet based on Atomic Functions have demonstrated successful performances in computer science. The ECG signals are segmented into 1, 2, 5, and 10 s; these segmented signals are fed into the classifier stage. Our proposal was tested in four accessible public databases separately, and finally by gathering the databases. We were able to successfully differentiate between 11 types of ECG signals with an accuracy of 98.9%.

Słowa kluczowe

EN

electrocardiography signal; wavelet; atomic function

PL

elektrokardiografia; falki; funkcja atomowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 2
• Strony: 803--814
• Opis fizyczny: Bibliogr. 60 poz., rys., tab., wykr.

Twórcy

autor

Hernandez-Matamoros Andres

• Iwate Prefectural University (IPU), Faculty of Software and Information Science, Iwate, Japan; Instituto Politecnico Nacional, Mexico D. F., Mexico

autor

Fujita Hamido

• Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam; Iwate Prefectural University (IPU), Faculty of Software and Information Science, Iwate, 020-0693, Japan

autor

Escamilla-Hernandez Enrique

• Instituto Politecnico Nacional, Mexico D. F., Mexico

autor

Perez-Meana Hector

• Instituto Politecnico Nacional, Mexico D. F., Mexico

autor

Nakano-Miyatake Mariko

• Instituto Politecnico Nacional, Mexico D. F., Mexico

Bibliografia

• [1] Acharya UR, Fujita H, Oh SL, Raghavendra U, Tan JH, Adam M, et al. Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network. Future Gener Comput Syst 2018;79:952–9. http://dx.doi.org/10.1016/j.future.2017.08.039.
• [2] Barill T. The six second ECG: a practical guidebook to basic ECG interpretation. Nursecom; 2003.
• [3] St.-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database: doi:10.13026/C2V88N, http://www.physionet.org/physiobank/database/incartdb/.
• [4] Pater C. Methodological considerations in the design of trials for safety assessment of new drugs and chemical entities. Curr Control Trials Cardiovasc Med 2005;1468–6694. http://dx.doi.org/10.1186/1468-6708-6-1.
• [5] Lynn P. Recursive digital filters for biological signals. Med Biol Eng Comput 1979;9(1):37–43.
• [6] Yelderman M, Widrow B, Cioffi JM, Hesler E, Leddy JA. ECG enhancement by adaptive cancellation of electrosurgical interference. IEEE Trans Biomed Eng 1983;30(7):392–8.
• [7] Sayadi O, Shamsollahi MB. Multiadaptive bionic wavelet transform: application to ECG denoising and baseline wandering reduction. EURASIP J Adv Signal Process 2007;2007(14):1–11.
• [8] Sameni R, Shamsollahi MB, Jutten C, Clifford GD. A nonlinear Bayesian filtering framework for ECG denoising. IEEE Trans Biomed Eng 2007;54(12):2172–85.
• [9] Kim H, Yazicioglu RF, Merken P, van Hoof C, Yoo H-J. ECG signal compression and classification algorithm with quad level vector for ECG holter system. IEEE Trans Inf Technol Biomed 2010;14(1):93–100.
• [10] Hu YH, Tompkins WJ, Urrusti JL, Afonso VX. Application of artificial neural networks for ECG signal detection and classification. J Eletrocardiol 1990;26(Suppl.):66–73.
• [11] Poli R, Cagnoni S, Valli G. Genetic design of optimum linear and nonlinear QRS detectors. IEEE Trans Biomed Eng 1995;42(11):1137–41.
• [12] Daubechies I. The wavelet transform, time/frequency location and signal analysis. IEEE Trans Inform Theory 1990;36(5):961–1005.
• [13] Martinez JP, Almeida R, Olmos S, Rocha AP, Laguna P. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans Biomed Eng 2004;51(4):570–81.
• [14] Afonso VX, Tompkins WJ, Nguyen TQ, Luo S. ECG beat detection using filter banks. IEEE Trans Biomed Eng 1999;46 (2):192–202.
• [15] Clifford GD, Azuaje F, McSharry P. Advanced methods and tools for ECG data analysis. 1st ed. Artech House Publishers; 2006.
• [16] Lin C-C, Yang C-M. Heartbeat classification using normalized RR intervals and morphological features. Math Problem Eng 2014;2014:1–11.
• [17] Korürek M, Dogan B. ECG beat classification using particle swarm optimization and radial basis function neural network. Expert Syst Appl 2010;37(12):7563–9.
• [18] Özbay Y, Tezel G. A new method for classification of ECG arrhythmias using neural network with adaptive activation function. Digit Signal Process 2010;20(4):1040–9.
• [19] Jolliffe I. Principal component analysis. second ed. Springer; 2002.
• [20] Kim J, Shin HS, Shin J, Lee M. Robust algorithm for arrhythmia classification in ECG using extreme learning machine. Biomed Eng Online 2009;8(1):1–12.
• [21] Kanaan L, Merheb D, Kallas M, Francis C, Amoud H, Honeine P. PCA and KPCA of ECG signals with binary SVM classification. IEEE Workshop on Signal Processing Systems (SiPS). 2011. pp. 344–8.
• [22] Sarfraz M, Khan AA, Li FF. Using independent component analysis to obtain feature space for reliable ECG arrhythmia classification. IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2014. pp. 62–7.
• [23] Übeyli ED. Combining recurrent neural networks with eigenvector methods for classification of ECG beats. Digit Signal Process 2009;19(2):320–9.
• [24] Ye C, Kumar BVK, Coimbra MT. Combining general multi- class and specific two-class classifiers for improved customized ECG heartbeat classification. International Conference on Pattern Recognition (ICPR). 2012. pp. 2428–31.
• [25] Ioffe S. Probabilistic linear discriminant analysis. Proc. European Conf. Computer Vision. 2006. pp. 531–42.
• [26] Mahesh V, Kandaswamy A, Vimal C, Sathish B. ECG arrhythmia classification based on logistic model tree. J Biomed Sci Eng 2009;2(6):405–11.
• [27] Lanatá A, Valenza G, Mancuso C, Scilingo EP. Robust multiple cardiac arrhythmia detection through bispectrum analysis. Expert Syst Appl 2011;38(6):6798–804.
• [28] Yeh Y-C, Chiou CW, Lin H-J. Analyzing ECG for cardiac arrhythmia using cluster analysis. Expert Syst Appl 2012;39 (1):1000–10.
• [29] Gomes PR, Soares FO, Correia JH, Lima CS. ECG data-acquisition and classification system by using wavelet- domain hidden markov models. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2010. pp. 4670–3.
• [30] Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, et al. A deep convolutional neural network model to classify heartbeats. Comput Biol Med 2017;89:389–96.
• [31] Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK. Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowledge Based Syst 2017;132:62–71. http://dx.doi.org/10.1016/j.knosys.2017.06.003.
• [32] Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci 2017;405:81–90. http://dx.doi.org/10.1016/j.ins.2017.04.012.
• [33] Oh Shu Lih, Ng Eddie YK, San Tan Ru, Rajendra Acharya U. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med 2018;102:278–87.
• [34] Oh SL, Ng EY, San Tan R, Acharya UR. Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types. Comput Biol Med 2019;105:92–101.
• [35] Pudukotai Dinakarrao SM, JantschAddhard A. Arrhythmia detection with digital hardware by learning ECG signal. Proceedings of the 2018 on Great Lakes Symposium on VLSI; 2018. p. 495–8.
• [36] Plawiak P. Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals. Swarm Evol Comput 2018;39:192–208.
• [37] Plawiak P. Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system. Expert Syst Appl 2018;92:334–49.
• [38] Yildirim Ö, Plawiak P, Tan R-S, Acharya UR. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med 2018;102:411–20.
• [39] Ibrahim Hamed, Mohamed Owis. Automatic arrhythmia detection using support vector machine based on discrete wavelet transform. J Med Imaging Health Inform 2016;6:204–9. http://dx.doi.org/10.1166/jmihi.2016.1611.
• [40] Alonso-Atienza F, Morgado E, Fernandez-Martinez L, Garcia-Alberola A, Rojo-Alvarez JL. Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng 2014;61 (3):832–40. http://dx.doi.org/10.1109/TBME.2013.2290800.
• [41] Kaveh A, Chung W. Automated classification of coronary atherosclerosis using single lead ECG. 2013 IEEE Conference on Wireless Sensor (ICWISE); 2013. pp. 108–13. http://dx.doi.org/10.1109/ICWISE.2013.6728790.
• [42] Martis Roshan Joy, Rajendra Acharya U, Mandana KM, Ray AK, Chakraborty Chandan. Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Syst Appl 2012;39(14):11792–800.
• [43] Babaoglu I, Findik O, Bayrak M. Effects of principle component analysis on assessment of coronary artery diseases using support vector machine. Expert Syst Appl 2010;37(3):2182–5. http://dx.doi.org/10.1016/j.eswa.2009.07.055.
• [44] Konovalov YY, Yurin AV. Orthogonal Kravchenko Wavelets in digital Signal and image procesing. Progress in Electromagnetics Research Symposium Proceedings 2009; 2009. p. 271–5. ISBN 978-1-934142-10-3.
• [45] 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(23):e215– 20 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full]; (June 13).
• [46] Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 2001;20(3):45–50.
• [47] Greenwald SD. The development and analysis of a ventricular fibrillation detector. Massachusetts Institute of Technology; 1986.
• [48] Kravchenko VF, Hector MP, Ponomaryov VI. Adaptive digital processing of multidimensional signals with applications. M: FIZMATLIT 2009;360. ISBN 978-5-9221-1170-6.
• [49] Juarez C, Ponomaryov V, Sanchez J, Kravchenko V. Wavelets based on atomic function used in detection and classification of masses in mammography. Lecture notes in artificial intelligence, Vol. LNAI 5317. Springer; 2008. p. 295–304.
• [50] Ponomaryov V, Ramos E. 3D video sequence reconstruction algorithms implemented on a DSP. Proc SPIE Int Soc Opt Eng 2011;7871:78711D.
• [51] Ponomarev Vladimir, Kravchenko Victor, Gomeztagle F. Super-resolution method based on wavelet atomic functions in images and video sequences. Telecommun Radio Eng 2009;68:747–61. http://dx.doi.org/10.1615/TelecomRadEng.v68.i9.10.
• [52] Kravchenko VF, Pustovoit VI. New system of Kravchenko orthogonal wavelets. Dokl Math 2009;80(October (2)):775–80. MAIK Nauka/Interperiodica.
• [53] Kuhn M, Johnson K, SpringerLink (Online service). Applied predictive modeling. New York, NY: Springer New York; 2013.
• [54] Kotsiantis SB, Kotsiantis SB. Decision trees: a recent overview. Artif Intell Rev 2013;39(4):261–83. http://dx.doi.org/10.1007/s10462-011-9272-4.
• [55] Aggarwal CC, editor. Data classification: algorithms and applications. Boca Raton: CRC Press; 2014.
• [56] Costantini G, Casali D, Massimiliano T. An SVM based classification method for EEG signals. Proceedings of the 14th WSEAS International Conference on Circuits; 2010. p. 107–9.
• [57] Barros Rodrigo C, Basgalupp MP, Carvalho ACPLF, Freitas Alex. A survey of evolutionary algorithms for decision-tree induction. Ieee Trans Syst Man Cybern Part C 2012;42 (3):291–312.
• [58] Novel Image Interest Points Detector Based On Harris Method Using Atomic Functions, Journal ‘‘Telecommunications and Radio Engineering’’, Begell House Inc., ISSN 0040-2508.
• [59] Iyengar N, Peng C-K, Morin R, Goldberger AL, Lipsitz LA. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am J Physiol 1996;271:1078–84.
• [60] Hsu C-W, Lin C-J. A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 2002;13 (March (2)):415–25.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).