Automated detection of myocardial infarction in ECG using modified Stockwell transform and phase distribution pattern from time-frequency analysis

• Autorzy: Swain Sushree Satvatee , Patra Dipti , Singh Yengkhom Omesh

Identyfikatory

DOI

10.1016/j.bbe.2020.06.004

• Języki publikacji: EN

Abstrakty

EN

Myocardial infarction (MI), usually referred as heart attack, takes place when blood circulation stops to specific portion of the heart resulting permanent damage to the heart muscles. It is an important task to identify the occurrence of MI from the ECG recordings efficiently. Most of the detection procedures include advanced signal processing methods, more ECG features and composite classifiers, making the overall procedure complex. This paper aims at automated identification of MI using modified Stockwell transform (MST) based time-frequency analysis and a phase information distribution pattern method. The morphologi-cal, pathological and temporal alterations in ECG waveforms resulting from the onset of MI are noticed in the phase distribution pattern of the ECG signal. Two discriminating features, utterly reflecting these alterations, are recognized for 12 leads of the MI affected ECG signal. Prior informations regarding the pathological characteristics of the specific disease are required for the correct detection of MI using few numbers of ECG leads. Thus, in this paper 12 lead ECG signals have been considered for identification of MI. The two-class classification problem with MI class and healthy individual class is performed using the threshold based classification regulation. Both healthy control and MI affected ECG signals are collected from the PTB diagnostic ECG database. The accuracy, sensitivity and specificity are found to be 99.93%, 99.97% and 99.30% for detection of MI. The proposed method has got the superiority in terms of simplicity of features, small feature dimension and simpler classification rule ensuring faster, accurate and easier MI detection.

Słowa kluczowe

EN

electrocardiogram; myocardial infarction; modified Stockwell transform; phase information

PL

elektrokardiogram; zawał mięśnia sercowego; transformacja Stockwella

• 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. 3
• Strony: 1174--1189
• Opis fizyczny: Bibliogr. 54 poz., rys., tab., wykr.

Twórcy

autor

Swain Sushree Satvatee

• Department of Electrical Engineering, National Institute of Technology, Rourkela, India

autor

Patra Dipti

• Department of Electrical Engineering, National Institute of Technology, Rourkela, India

autor

Singh Yengkhom Omesh

• Department of Electrical Engineering, National Institute of Technology, Rourkela, India

Bibliografia

• [1] Manikandan MS, Dandapat S. Wavelet energy based diagnostic distortion measure for ECG. Biomed Signal Process Control 2007;2(2):80–96.
• [2] Sharma M, Tan RS, Acharya UR. A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank. Comput Biol Med 2018;102(June):341–56.
• [3] Goldberger AL. Clinical electrocardiography: a simplified approach. New York, NY, USA: Elsevier Health Sciences; 2012.
• [4] Acharya UR, Hagiwara Y, Koh JEW, Oh SL, Tan JH, Adam M, et al. Entropies for automated detection of coronary artery disease using ecg signals: a review. Biocybern Biomed Eng 2018;38(2):373–84.
• [5] WHOfactsheet. The top ten causes of death, fact sheet-310; 2017, Available from: http://wwwwhoint/mediacentre/factsheets/fs310/en/.
• [6] Hall JE. Text book of medical physiology. 11th Ed. New York, NY, USA: Elsevier Health Sciences; 2006.
• [7] Nallikuzhy JJ, Dandapat S. Spatial enhancement of ECG using diagnostic similarity score based lead selective multi-scale linear model. Comput Biol Med 2017;85(March):53–62. http://dx.doi.org/10.1016/j.compbiomed.2017.04.002.
• [8] Thygesen K, et al. Third universal definition of myocardial infarction. Eur Heart J 2012;33(20):2551–67.
• [9] Arif M, Malagore Ia, Afsar Fa. Detection and localization of myocardial infarction using K-nearest neighbor classifier. J Med Syst 2012;36(1):279–89.
• [10] Muhammad A, Malagore IA, Afsar FA. Automatic detection and localization of myocardial infarction using back propagation neural networks. 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010; 2010.
• [11] Safdarian N, Dabanloo NJ, Attarodi G. A new pattern recognition method for detection and localization of myocardial infarction using T-wave integral and total integral as extracted features from one cycle of ECG signal. J Biomed Sci Eng 2014;07(10):818–24.
• [12] Remya RS, Indiradevi KP, Babu KKA. Classification of myocardial infarction using multi resolution wavelet analysis of ECG. Proc Technol 2016;24:949–56.
• [13] Diker A, Comert Z, Avci E, Velappan S. Intelligent system based on genetic algorithm and support vector machine for detection of myocardial infarction from ECG signals. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 2018. pp. 1–4.
• [14] Dohare AK, Kumar V, Kumar R. Detection of myocardial infarction in 12 lead ECG using support vector machine. Appl Soft Comput J 2018;64:138–47. http://dx.doi.org/10.1016/j.asoc.2017.12.001.
• [15] Banerjee S, Mitra M. Application of cross wavelet transform for ECG pattern analysis and classification. IEEE Trans Instrum Meas 2014;63(2):326–33.
• [16] Bhaskar NA. Performance analysis of support vector machine and neural networks in detection of myocardial infarction. Proc Comput Sci 2014;20–30. Icict.
• [17] Acharya UR, Fujita H, Sudarshan VK, Oh SL, Adam M, Koh JEW, et al. Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl-Based Syst 2016;99(February):146–56.
• [18] Sharma LN, Tripathy RK, Dandapat S. Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans Biomed Eng 2015;62 (7):1827–37.
• [19] Sadhukhan D, Pal S, Mitra M. Automated identification of myocardial infarction using harmonic phase distribution pattern of ECG data. IEEE Trans Instrum Meas 2018;67 (10):2303–13.
• [20] Swain SS, Patra D. Multiscale energy based suitable wavelet selection for detection of myocardial infarction in ECG. Healthc Technol Lett 2018;6(1):1–7.
• [21] Tripathy RK, Bhattacharyya A, Pachori RB. A novel approach for detection of myocardial infarction from ECG signals of multiple electrodes. IEEE Sens J 2019;19(12):4509–17.
• [22] Jayachandran ES, K PJ, Acharya RU. Analysis of myocardial infarction using discrete wavelet transform; 2010;985–92.
• [23] Han C, Shi L. Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features. Comput Methods Programs Biomed 2019;175:9–23.
• [24] Tripathy RK, Dandapat S. Detection of cardiac abnormalities from multilead ECG using multiscale phase alternation features. J Med Syst 2016;40(6).
• [25] Tripathy RK, Dandapat S. Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features. Healthc Technol Lett 2017;4(2):57–63. http://dx.doi.org/10.1049/htl.2016.0089.
• [26] Padhy S, Dandapat S. Third-order tensor based analysis of multilead ECG for classification of myocardial infarction. Biomed Signal Process Control 2017;31:71–8.
• [27] Sharma LD, Sunkaria RK. Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal Image Video Process 2017;12 (2):199–206.
• [28] Kumar M, Pachori RB, Acharya UR. Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework. Entropy 2017;19(9).
• [29] Sun L, Lu Y, Yang K, Li S. ECG analysis using multiple instance learning for myocardial infarction detection. IEEE Trans Biomed Eng 2012;59(12):3348–56.
• [30] Liu B, Liu J, Wang G, Huang K, Li F, Zheng Y, et al. A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Comput Biol Med 2015;61:178–84.
• [31] Chang PC, Lin JJ, Hsieh JC, Weng J. Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models. Appl Soft Comput J 2012;12(10):3165–75.
• [32] Kora P, Kalva SR. Improved bat algorithm for the detection of myocardial infarction. SpringerPlus 2015;4(1):1–18.
• [33] Kora P. ECG based myocardial infarction detection using hybrid firefly algorithm. Comput Methods Programs Biomed 2017;152:141–8.
• [34] Liu W, Huang Q, Chang S, Wang H, He J. Multiple-feature- branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram. Biomed Signal Process Control 2018;45:22–32. http://dx.doi.org/10.1016/j.bspc.2018.05.013.
• [35] Sharma RR, Kumar A, Pachori RB, Acharya UR. Accurate automated detection of congestive heart failure using eigenvalue decomposition based features extracted from HRV signals. Biocybern Biomed Eng 2019;39(2):312–27.
• [36] Plawiak P. Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals. Neural Comput Appl 2019;2.
• [37] Plawiak P. Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals. Swarm Evol Comput 2018;39(October 2017):192–208.
• [38] Abdar M. Novel methodology for cardiac arrhythmias classification based on long- duration ECG signal fragments analysis chapter in book: biomedical signal processing – advances in theory, algorithms, and applications novel methodology for cardiac arrhythmias classification based on long-duration ECG signal fragments analysis. February 2019; 2020.
• [39] Plawiak P. Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system, vol. 92. 2018;p. 334–49.
• [40] Tuncer T, Dogan S, Plawiak P, Acharya UR. Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals? Knowl-Based Syst 2019;186:104923.
• [41] Moeinzadeh H, Gargiulo GD, Gunnam S. Towards real-time heartbeat classification: evaluation of nonlinear morphological features and voting method; 2019;1–27.
• [42] Sahu SS, Panda G, George NV. An improved S-transform for time-frequency analysis. 2009 IEEE International Advance Computing Conference, IACC 2009; 2009. pp. 315–9.
• [43] Wang YH, et al. 'The tutorial: S transform', graduate institute of communication engineering. Taipei: National Taiwan University; 2010. p. 1–23.
• [44] Ahrabian A, Looney D, Stankovic´ L, Mandic DP. Synchrosqueezing-based time-frequency analysis of multivariate data. Signal Process 2015;106:331–41.
• [45] Stockwell R, Mansinha L, Lowe R. Localisation of the complex spectrum: the s transform. J Assoc Explor Geophys 1996;17(3):99–114.
• [46] Zhu H, Goodyear B, Lauzon M, Brown R, Mayer G, Law A, et al. A new local multiscale Fourier analysis for medical imaging. Med Phys 2003;30(6):1134–41.
• [47] Stockwell RG. A basis for efficient representation of the s-transform. Digit Signal Process 2007;17(1):371–93.
• [48] Singh YO, Swain SS, Patra D. Time-frequency analysis based detection of dysrhythmia in ECG using stockwell transform, vol. 11942 LNCS. Springer International Publishing; 2019.
• [49] Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 2000;101(23):e215–20.
• [50] Oeff M, Koch H, Bousseljot R, Kreiseler D. 'The ptb diagnostic ecg database', national metrology institute of Germany; 2012, http://www.physionet.org/physiobank/database/ptbdb.
• [51] Assous S, Boashash B. Evaluation of the modified S-transform for timefrequency synchrony analysis and source localisation. Eurasip J Adv Signal Process 2012 2012;1:1–18.
• [52] Mansinha L, Stockwell RG, Lowe RP. Pattern analysis with two-dimensional spectral localisation: applications of two-dimensional S transforms. Phys A: Stat Mech Appl 1997;239 (1–3):286–95.
• [53] McFadden PD, Cook JG, Forster LM. Decomposition of gear vibration signals by the generalized S transform. Mech Syst Signal Process 1999;13(5):691–707.
• [54] Pinnegar CR, Mansinha L. The S-transform with windows of arbitrary and varying shape. Geophysics 2003;68(1):381–5.

Uwagi

PL

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