Abstract
Interrupted blood flow to regions of the heart causes damage to heart muscles, resulting in myocardial infarction (MI). MI is a major source of death worldwide. Accurate and timely detection of MI facilitates initiation of emergency revascularization in acute MI and early secondary prevention therapy in established MI. In both acute and ambulatory settings, the electrocardiogram (ECG) is a standard data type for diagnosis. ECG abnormalities associated with MI can be subtle, and may escape detection upon clinical reading. Experience and training are required to visually extract salient information present in the ECG signals. This process of characterization is manually intensive, and prone to intra-and inter-observer-variability. The clinical problem can be posed as one of diagnostic classification of MI versus no MI on the ECG, which is amenable to computational solutions. Computer Aided Diagnosis (CAD) systems are designed to be automated, rapid, efficient, and ultimately cost-effective systems that can be employed to detect ECG abnormalities associated with MI. In this work, ECGs from 200 subjects were analyzed (52 normal and 148 MI). The proposed methodology involves pre-processing of signals and subsequent detection of R peaks using the Pan-Tompkins algorithm. Nonlinear features were extracted. The extracted features were ranked based on Student’s t-test and input to k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), and Decision Tree (DT) classifiers for distinguishing normal versus MI classes. This method yielded the highest accuracy 97.96%, sensitivity 98.89%, and specificity 93.80% using the SVM classifier.
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The data used in this study is available in the publicly available PTB ECG database.
References
Acharya UR, Chua ECP, Faust O, Lim TC, Lim LFB (2011) Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters. Physiol Meas. https://doi.org/10.1088/0967-3334/32/3/002
Acharya UR, Faust O, Sree SV, Ghista DN, Dua S, Joseph P et al (2013) An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes. Comput Methods Biomech Biomed Eng 16:222–234. https://doi.org/10.1080/10255842.2011.616945
Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JEW, Adeli A (2015) Computer-aided diagnosis of depression using EEG signals. Eur Neurol. https://doi.org/10.1159/000381950
Acharya UR et al (2016a) Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowledge Based Syst 99(2016):146–156. https://doi.org/10.1016/j.procs.2015.01.043
Acharya UR, Fujita H, Sudarshan VK, Lih OS, Adam M (2016b) Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl Based Syst 9(8):1–39. https://doi.org/10.1016/j.knosys.2016.01.040
Acharya UR et al (2017a) Application of higher-order spectra for the characterization of Coronary artery disease using electrocardiogram signals. Biomed Signal Process Control. https://doi.org/10.1016/j.artmed.2019.07.006
Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M (2017b) Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf Sci 415–416:190–198. https://doi.org/10.1016/j.ins.2017.06.027
Acharya UR, Fujita H, Adam M, Lih OS, Sudarshan VK et al (2017c) Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: a comparative study. Inf Sci 377:17–29. https://doi.org/10.1016/j.ins.2016.10.013
Acharya UR et al (2017d) Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: a comparative study. Inf Sci 377:17–29. https://doi.org/10.1016/j.ins.2016.10.013
Acharya UR et al (2017e) Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal. Knowl Based Syst 132:156–166. https://doi.org/10.1016/j.knosys.2017.06.026
Acharya UR et al (2018a) Entropies for automated detection of coronary artery disease using ECG signals: a review. Biocybern Biomed Eng. https://doi.org/10.1016/j.bbe.2018.03.001
Acharya UR et al (2018b) Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2017.08.039
Acharya UR et al (2019) Application of nonlinear methods to discriminate fractionated electrograms in paroxysmal versus persistent atrial fibrillation. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2019.04.018
Alghamdi A et al (2020) Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-08769-x
Ansari S, Farzaneh N, Duda M, Horan K, Andersson HB, Goldberger ZD, Nallamothu BK, Najarian K (2017) A review of automated methods for detection of myocardial ischemia and infarction using electrocardiogram and electronic health records. IEEE Rev Biomed Eng 10:264–298. https://doi.org/10.1109/RBME.2017.2757953
Baloglu UB, Talo M, Yildirim O, Tan RS, Acharya UR (2019) Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recognit Lett 122:23–30. https://doi.org/10.1016/j.patrec.2019.02.016
Bandt C, Pompe B (2002) Permutation entropy: a natural complexity measure for time series. J Physiol 88(16):3–17. https://doi.org/10.1103/PhysRevLett.88.174102
Bharadwaj AV, Upadhyaya SM, Sharath L, Srinivasan R (2018) Early diagnosis and automated analysis of myocardial infarction (STEMI) by detection of ST segment elevation using wavelet transform and feature extraction. Int Conf Des Innov 3Cs Comput Commun Control ICDI3C, pp 24–28. https://doi.org/10.1109/ICDI3C.2018.00014
Bhaskar NA (2015) Performance analysis of support vector machine and neural networks in detection of myocardial infarction. Procedia Comput Sci 46:20–30
Bishop CM (2006) Pattern recognition and machine learning. Springer-Verlag, Berlin, Heidelberg
Chua KC, Chandran V, Acharya UR, Lim CM (2010) Application of higher order statistics/spectra in biomedical signals—a review. Med Eng Phys 32:679–689. https://doi.org/10.1016/j.medengphy.2010.04.009
Clifford GD, Azuaje F, McSharry P (2006) Advanced methods and tools for ECG data analysis. Artech House, Norwood
Constantinescu L, Jinman K (2012) Feng DD (2012) Spark Medical: a framework for dynamic integration of multimedia medical data in to distributed m-health systems. IEEE Transl Inf Technol Biomed 16:40–52. https://doi.org/10.1109/TITB.2011.2174064
Dohare AK, Kumar V, Kumar R (2018) Detection of myocardial infarction in 12 lead ECG using support vector machine. Appl Soft Comput 64:138–147. https://doi.org/10.1016/j.asoc.2017.12.001
Feng K, Pi X, Liu H, Sun K (2019) Myocardial infarction classification based on convolutional neural network and recurrent neural network. Appl Sci 9(9):1–12. https://doi.org/10.3390/app9091879
Fu L, Lu B, Nie B, Peng Z, Liu H, Xi P (2020) Hybrid network with attention mechanism for detection and location of myocardial infarction based on 12-lead electrocardiogram signals. Sensors (Basel). https://doi.org/10.3390/s20041020
Fukushima K, Miyake S (1982) Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition, In: Competition and cooperation in neural nets. Springer, pp 267–285. https://doi.org/10.1007/BF00344251
Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC (2000) PhysioBank, Physiotool Kit, and Physionet components of a new research resource for complex physiologic signals. Circulation 101(4):215–220
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge
Haddadi R, Abdelmounim E, El Hanine M, Belaguid A (2019) A wavelet-based ECG delineation and automated diagnosis of myocardial infarction in PTB database, 2284216
Hagiwara Y, Fujita H, Oh SL, Acharya UR et al (2018) Computer-aided diagnosis of atrial fibrillation based on ECG signals: a review. Inf Sci 32(12):2–38. https://doi.org/10.1016/j.ins.2018.07.063
Han C, Shi L (2019) Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features. Comput Methods Programs Biomed 175:9–23. https://doi.org/10.1016/j.cmpb.2019.03.012
Han C, Shi L (2020) ML–ResNet: a novel network to detect and locate myocardial infarction using 12 leads ECG. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2019.105138
Hariharan M, Vijean V, Yaacob (2012) Objective analysis of vision impairments using single trial VEPs. Int Conf Biomed Eng (ICoBE). https://doi.org/10.1109/CCDC.2013.6561736
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647
Hinton GE, Osindero S, The YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527
Hopfield JJ (1987) Neural networks and physical systems with emergent collective computational abilities, in: spin glass theory and beyond: an introduction to the replica method and its applications. World Sci. https://doi.org/10.1073/pnas.79.8.2554
Hu M, Liang H (2012) Adaptive multiscale entropy analysis of multivariate neural data. IEEE Trans Biomed Eng 59:12–15. https://doi.org/10.1109/TBME.2011.2162511
Huang JS, Chen BQ, Zeng NY, Cao XC, Li Y (2020) Accurate classification of ECG arrhythmia using MOWPT enhanced fast compression deep learning networks. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02110-y
Hurst HE (1956) Methods of using long-term storage in reservoirs. Proc Inst Civ Eng 5(5):519–543. https://doi.org/10.1680/iicep.1956.11503
Jahmunah V et al (2019a) Automated detection of schizophrenia using nonlinear signal processing method. Artif Intell Med. https://doi.org/10.1016/j.artmed.2019.07.006
Jahmunah V et al (2019b) Computer-aided diagnosis of congestive heart failure using ECG signals—a review. Phys Med. https://doi.org/10.1016/j.ejmp.2019.05.004
Kannathal N, Lim CM, Rajendra Acharya U, Sadasivan PK (2006) Cardiac state diagnosis using adaptive neuro-fuzzy technique. Med Eng Phys. https://doi.org/10.1109/IEMBS.2005.1615304
Kora P (2017) ECG based myocardial infarction detection using hybrid firefly algorithm. Comput Methods Programs Biomed 152:141–148
Kumar N, Pachori RB, Acharya UR (2017) Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework. Entropy. https://doi.org/10.3390/e19090488
Labate D, Foresta FL, Occhiuto G, Morabito FC, Lay-Ekuakille A, Vergallo P (2013) Empirical mode decomposition vs. wavelet decomposition for the extraction of respiratory signal from single-channel ECG: a comparison. IEEE Sens J 13(7):2666–2674. https://doi.org/10.1109/JSEN.2013.2257742
Lecun Y, Bengio Y (1995) Convolutional networks for images, speech, and time series. In: Arbib MA (ed) The hand book on brain theory neural networks. MIT Press
Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539
Ley K (2015) Atherosclerosis, thrombosis and vascular biology. J Am Heart Assoc 15(9):436–448. https://doi.org/10.1161/ATVBAHA.117.309813
Lih OS, Jahmunah V, San TR, Ciaccio EJ, Yamakawa T, Tanabe M, Kobayashi M, Faust O (2020) Comprehensive electrocardiographic diagnosis based on deep learning. Artif Intell Med 103:101789
Lin Z, Gao Y, Chen Y, Ge Q, Mahara G, Zhang J (2020) Automated detection of myocardial infarction using robust features extracted from 12-lead ECG. Signal Image Video Process. https://doi.org/10.1007/s11760-019-01617-y
Liu C, Wang H, Lu Z (2013) EEG classification for multiclass motor imagery BCI. Control and decision conference (CCDC) 25th Chinese, pp 4450–4453. https://doi.org/10.1109/CCDC.2013.6561736
Liu B, Liu J, Wang G, Huang K, Li F et al (2014) A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Jpn J Clin Med 27(12):2892–2900. https://doi.org/10.1016/j.compbiomed.2014.08.010
Liu W, Huang Q, Chang S, Wang H, He J (2018a) Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram. Biomed Signal Process Control 45:22–32. https://doi.org/10.1016/j.bspc.2018.05.013
Liu W et al (2018b) Real-time multilead convolutional neural network for myocardial infarction detection. IEEE J Biomed Heal Inf 22(5):1434–1444. https://doi.org/10.1109/JBHI.2017.2771768
Liu W, Wang F, Huang Q, Chang S, Wang H, He J (2020) MFB-CBRNN: a hybrid network for MI detection using 12-Lead ECGs. IEEE J Biomed Health Inf 24(2):503–514. https://doi.org/10.1109/JBHI.2019.2910082
Lodhi AM et al (2018) A novel approach using voting from ECG leads to detect myocardial infarction. IntelliSys. https://doi.org/10.1007/978-3-030-01057-7_27
Lui HW, Chow KL (2018) Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices. Inf Med Unlocked 13:26–33. https://doi.org/10.1016/j.imu.2018.08.002
Martis JR, Acharya UR, Adeli H (2014) Current methods in electrocardiogram characterization. Comput Biol Med 48:133–149. https://doi.org/10.1016/j.compbiomed.2014.02.012
Murat F, Yildirim O, Talo M, Baloglu UB, Demir Y, Acharya UR (2020) Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review. Comput Biol Med 120:103726
Padhy S, Dandapat S (2017) Third-order tensor based analysis of multilead ECG for classification of myocardial infarction. Biomed Signal Process Control 31:71–78. https://doi.org/10.1016/j.bspc.2016.07.007
Pal S, Mitra M (2012) Empirical mode decomposition based ECG enhancement and QRS detection. Comput Biol Med 42(1):83–92. https://doi.org/10.1016/j.compbiomed.2011.10.012
Pan J, Tompkins WJ (1985) A real time QRS detection algorithm. IEEE Trans Biomed Eng 32(3):230–236. https://doi.org/10.1109/TBME.1985.325532
Pandey S, Voorsluys W, Niu S, Khandoker A, Buyya R (2012) An autonomic cloud environment for hosting ECG data analysis services. Future Gener Comput Syst 28:147–154. https://doi.org/10.1016/j.future.2011.04.022
Paul JK, Iype T, Hagiwara DRY, Koh JEW, Acharya UR (2019) Characterization of fibromyalgia using sleep EEG signals with nonlinear dynamical features. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2019.103331
Peng CK, Havlin S, Hausdorf JM, Mietus JE et al (1996) Fractal mechanisms and heart rate dynamics. J Electro Cardiol 8(15):59–64. https://doi.org/10.1016/S0022-0736(95)80017-4
Pham TH, Vicnesh J, Wei JKE, Oh SL, Arunkumar N, Abdulhay EW (2020a) Autism spectrum disorder diagnostic system using HOS bispectrum with EEG signals. Int J Environ Res Public Health 17(3):971
Pham TH, Raghavendra U, Koh JEW, Gudigar A, Chan WY, Hamid MTR, Rahmat K, Fadzil F, Ng KH, Ooi CP, Ciaccio EJ, Fujita H, Acharya UR (2020b) Development of breast papillary index for differentiation of benign and malignant lesions using ultrasound images. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02310-6
Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci 88(12):2297–2301. https://doi.org/10.1073/pnas.88.6.2297
Ramesh G, Satyanarayana D, Sailaja M (2020) Composite feature vector based cardiac arrhythmia classification using convolutional neural networks. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02259-6
Reasat T, Shahnaz C (2018) Detection of inferior myocardial infarction using shallow convolutional neural networks. In: 5th IEEE Reg 10 Humanit Technol Conf 2017, R10-HTC 2017, pp 718–721. https://doi.org/10.1109/R10-HTC.2017.8289058
Renyi A (1961) On measures of entropy and information. Math Stat Probab 1(4):547–561
Ribeiro AH, Ribeiro MH, Paixão GMM, Oliveira DM, Gomes PR, Canazart JA (2020) Automatic diagnosis of the 12-lead ECG using a deep neural network. Nature Commun 11(1):1–9
Richman JS, Randall MJ (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278:H2039–H2049
Robert H, Mangner N, Schuler G, Erbs S (2013) Physical exercise training and coronary artery disease. Rev Health Care 4(3):1–17
Roger VL (2007) Epidemiology of myocardial infarction. Med Clin North Am 91(7):537–552. https://doi.org/10.1016/j.mcna.2007.03.007
Rosenstein M, Colins JJ, Luca CJ (1993) A practical method for calculating largest Lyapunov exponents from small data sets. Physica D 65:117–134. https://doi.org/10.1016/0167-2789(93)90009-P
Rosso OA, Blanco S, Yordanova J, Kolev V et al (2001) Wavelet entropy: a new tool for analysis of short duration electrical signals. J Neurosci Methods 105:65–75. https://doi.org/10.1016/S0165-0270(00)00356-3
Sadhukhan D, Pal S, Mitra M (2018) Automated identification of myocardial infarction using harmonic phase distribution pattern of ECG Data. IEEE Trans Instrum Meas 67(10):2303–2313. https://doi.org/10.1109/TIM.2018.2816458
Sankari Z, Adeli H (2011) Heart Saver: a mobile cardiac monitoring system for auto-detection of arial fibrillation, myocardial infarction, and atrio-ventricular block. Comput Biol Med 41:211–220. https://doi.org/10.1016/j.compbiomed.2011.02.002
Seenivasagam V, Chitra R (2016) Myocardial infarction detection using intelligent algorithms. Neural Netw World 26(1):91–110
Setiawan NA, Prabowo DW, Nugroho HA (2014) Benchmarking of feature selection techniques for coronary artery disease diagnosis. In: 6th International Conference on Information Technology and Electrical Engineering, Yogyakarta, Indonesia, vol 3(1), pp 123–131. https://doi.org/10.1109/ICITEED.2014.7007898
Shannon C (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Sharma LN, Tripathy RK, Dandapat S (2015) Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans Biomed Eng 62(7):1827–1837
Sharma M, Tan RS, Acharya UR (2018) A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank. Comput Biol Med 102:341–356. https://doi.org/10.1016/j.compbiomed.2018.07.005
Song Y, Liò P (2010) A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. J Biomed Sci Eng 3(6):556–567. https://doi.org/10.1152/ajpheart.2000.278.6.H2039
Strodthoff N, Strodthoff C (2019) Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiol Meas 40(1):1–11. https://doi.org/10.1088/1361-6579/aaf34d
Student Biometrika (1908) The probable error of a mean. Biometrika 6(1):1–25. https://www.jstor.org/stable/2331554
Tamura T (2012) Home geriatric physiological measurements. Physiol Meas 33(10):R47. https://doi.org/10.1088/0967-3334/33/10/R47
Thomas FL (2015) Myocardial infarction: mechanisms, diagnosis, and complications. Eur Heart J 36(16):947–949. https://doi.org/10.1093/eurheartj/ehv071
Tripathy RK, Bhattacharyya A, Pachori RB (2019) A novel approach for detection of myocardial infarction from ECG signals of multiple electrodes. IEEE Sens J 19(12):4509–4517
Venkatesan C, Karthigaikumar P, Satheeskumaran S (2018) Mobile cloud computing for ECG telemonitoring and real-time coronary heart disease risk detection. Biomed Signal Process Control 44:138–145. https://doi.org/10.1016/j.bspc.2018.04.013
Venu K, Natesan P, Krishnakumar B, Sasipriya N (2019) Classification of myocardial infarction using convolution neural network. Int J Recent Technol Eng 8(4):12763–12768
Webber CL, Zbilut JP (1994) Dynamical assessment of physiological systems and states using recurrence plot strategies. J Appl Physiol 76(2):965–973. https://doi.org/10.1152/jappl.1994.76.2.965
WHO fact sheet (2012) https://www.who.int/mediacentre/factsheets/fs310/en/index.html. Accessed 19 Jan 2016
Zbilut JP, Webber CL (1992) Embeddings and delays as derived from quantification of recurrence plots. Phys Lett 171(4):199–203. https://doi.org/10.1016/0375-9601(92)90426-M
Zbilut JP, Thomasson N, Webber CL (2002) Recurrence quantification analysis as a tool for nonlinear exploration of nonstationary cardiac signals. Med Eng Phys 24(4):53–60. https://doi.org/10.1016/S1350-4533(01)00112-6
Zhang J et al (2019) Automated detection and localization of myocardial infarction with staked sparse autoencoder and treebagger. IEEE Access 7:70634–70642. https://doi.org/10.1109/ACCESS.2019.2919068
Ziv J, Lempel A (1977) A universal algorithm for sequential data compression. IEEE Trans Inf Theory 23:337–343. https://doi.org/10.1109/TIT.1977.1055714
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Sridhar, C., Lih, O.S., Jahmunah, V. et al. Accurate detection of myocardial infarction using non linear features with ECG signals. J Ambient Intell Human Comput 12, 3227–3244 (2021). https://doi.org/10.1007/s12652-020-02536-4
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DOI: https://doi.org/10.1007/s12652-020-02536-4