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A Comparative Study of Myocardial Infarction Detection from ECG Data Using Machine Learning

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Advanced Computing and Intelligent Technologies

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 218))

Abstract

Myocardial Infarction (MI) is a life-threatening heart disease, timely medical intervention of which can reduce the mortality rate. It can be detected from Electrocardiogram or ECG. Diagnostic methods of this disease by clinical approaches are typically invasive. They also do not fulfill the detection accuracy, and there is a chance of human error. In the medical field, machine learning techniques have great potential for disease diagnosis. We can achieve accurate detection from ECG by using deep learning methods. In this paper, we did a comprehensive comparative study of a few existing proposals with different approaches to detect and predict Myocardial infarction. And then we proposed a deep learning approach for future implementation in order to achieve superior performance.

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References

  1. Global Action Plan for the prevention and control of noncommunicable diseases 2013–2020. World Health Organization, Gendva, Switzerland (2013)

    Google Scholar 

  2. Laslett, L.J., Alagona, P., Clark, B.A., Drozda, J.P., Saldivar, F., Wilson, S.R., Hart, M.: The worldwide environment of cardiovascular disease: prevalence, diagnosis, therapy, and policy issues: a report from the American College of Cardiology. J. Am. Coll. Cardiol. 60(25 Supplement), S1–S49 (2012)

    Article  Google Scholar 

  3. Siddiqui, S.Y., Athar, A., Khan, M.A., Abbas, S., Saeed, Y., Khan, M.F., Hussain, M.: Modelling, simulation and optimization of diagnosis cardiovascular disease using computational intelligence approaches. J. Med. Imag. Health Inf. 10(5), 1005–1022 (2020)

    Google Scholar 

  4. Muniasamy, A., Muniasamy, V., Bhatnagar, R.: Predictive analytics for cardiovascular disease diagnosis using machine learning techniques. In: International Conference on Advanced Machine Learning Technologies and Applications, pp. 493–502. Springer, Singapore (2020

    Google Scholar 

  5. Wang, J., Liu, C., Li, L., Li, W., Yao, L., Li, H., Zhang, H.: A stacking-based model for non-invasive detection of coronary heart disease. IEEE Access 8, 37124–37133 (2020)

    Article  Google Scholar 

  6. Sörnmo, L., Laguna, P.: Electrocardiogram (ECG) signal processing. Wiley encyclopedia of biomedical engineering (2006)

    Google Scholar 

  7. Sadhukhan, D., Pal, S., Mitra, M.: Automated identification of myocardial infarction using harmonic phase distribution pattern of ECG data. IEEE Trans Instrum. Measure. 67(10), 2303–2313 (2018)

    Article  Google Scholar 

  8. Biel, L., Pettersson, O., Philipson, L., Wide, P.: ECG analysis: a new approach in human identification. IEEE Trans Instrum. Measure. 50(3), 808–812 (2001)

    Article  Google Scholar 

  9. Kalyakulina, A.I., Yusipov, I.I., Moskalenko, V.A., Nikolskiy, A.V., Kosonogov, K.A., Osipov, G.V., Ivanchenko, M.V.: LUDB: A new open-access validation tool for electrocardiogram delineation algorithms. IEEE Access 8, 186181–186190 (2020)

    Google Scholar 

  10. Blanco, A.L.A., Grautoff, S., Hermann, T.: ECG sonification to support the diagnosis and monitoring of myocardial infarction. J. Multimodal User Interf., 1–12

    Google Scholar 

  11. Demir, V., Turan, Y., Ede, H., Hidayet, S., Erkoç, M.F.: Electrocardiographic changes in right ventricular metastatic cardiac tumor mimicking acute ST elevation myocardial infarction: a case of misdiagnosis. Turkish J. Emerg. Med. 19(1), 33–35 (2019)

    Article  Google Scholar 

  12. Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220

    Google Scholar 

  13. Kim, Y.G., Shin, D., Park, M.Y., Lee, S., Jeon, M.S., Yoon, D., Park, R.W.: ECG-ViEW II, a freely accessible electrocardiogram database. PLoS ONE 12(4), (2017)

    Article  Google Scholar 

  14. Dohare, A.K., Kumar, V., Kumar, R.: Detection of myocardial infarction in 12 lead ECG using support vector machine. Appl. Soft Comput. 64, 138–147 (2018)

    Article  Google Scholar 

  15. Wang, Z., Qian, L., Han, C., Shi, L.: Application of multi-feature fusion and random forests to the automated detection of myocardial infarction. Cognit. Syst. Res. 59, 15–26 (2020)

    Article  Google Scholar 

  16. Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 3, 230–236 (1985)

    Article  Google Scholar 

  17. Biau, G., Scornet, E.: A random forest guided tour. Test 25(2), 197–227 (2016)

    Article  MathSciNet  Google Scholar 

  18. Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9(7), 1545–1588 (1997)

    Article  Google Scholar 

  19. Baloglu, U.B., Talo, M., Yildirim, O., San Tan, R., Acharya, U.R.: Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Patt. Recognit. Lett. 122, 23–30 (2019)

    Article  Google Scholar 

  20. Kora, P., Annavarapu, A., Borra, S.: ECG based myocardial infarction detection using different classification techniques. In: Classification in BioApps, pp. 57–77. Springer, Cham (2018)

    Google Scholar 

  21. Zupan, J.: Introduction to artificial neural network (ANN) methods: what they are and how to use them. Acta Chim. Slov. 41, 327–327 (1994)

    Google Scholar 

  22. Johnson, J.M., Khoshgoftaar, T.M.: Survey on deep learning with class imbalance. J. Big Data 6(1), 27 (2019)

    Article  Google Scholar 

  23. Savalia, S., Acosta, E., Emamian, V.: Classification of cardiovascular disease using feature extraction and artificial neural networks. J. Biosci. Med. 5(11), 64–79 (2017)

    Google Scholar 

  24. Mandal, S., Biswas, S., Balas, V.E., Shaw, R.N., Ghosh, A.: Motion prediction for autonomous vehicles from lyft dataset using deep learning. In: 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, pp. 768–773 (2020). https://doi.org/10.1109/iccca49541.2020.9250790

  25. Al-Zaiti, S., Besomi, L., Bouzid, Z., Faramand, Z., Frisch, S., Martin-Gill, C., Sejdić, E.: Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram. Nat. Commun. 11(1), 1–10 (2020)

    Google Scholar 

  26. Sharma, M., San Tan, R., Acharya, U.R.: A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank. Comput. Biol. Med. 102, 341–356 (2018)

    Article  Google Scholar 

  27. Kumar, M., Shenbagaraman, V.M., Shaw, R.N., Ghosh, A.: Predictive data analysis for energy management of a smart factory leading to sustainability. In: Favorskaya, M., Mekhilef, S., Pandey, R., Singh, N. (eds.) Innovations in Electrical and Electronic Engineering. Lecture Notes in Electrical Engineering, vol. 661. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-4692-1_58

  28. Kora, P., Krishna, K.S.R.: ECG based heart arrhythmia detection using wavelet coherence and bat algorithm. Sens. Imag. 17(1), 12 (2016)

    Article  Google Scholar 

  29. Mandal, S., Balas, V.E., Shaw, R.N., Ghosh, A.: Prediction analysis of idiopathic pulmonary fibrosis progression from OSIC dataset. In: 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON), Greater Noida, India, pp. 861–865 (2020). https://doi.org/10.1109/gucon48875.2020.9231239

  30. Canto, J.G., Rogers, W.J., Goldberg, R.J., Peterson, E.D., Wenger, N.K., Vaccarino, V., NRMI Investigators: Association of age and sex with myocardial infarction symptom presentation and in-hospital mortality. Jama 307(8), 813–822 (2012)

    Google Scholar 

  31. Čulić, V., Eterović, D., Mirić, D., Silić, N.: Symptom presentation of acute myocardial infarction: influence of sex, age, and risk factors. Am. Heart J. 144(6), 1012–1017 (2002)

    Article  Google Scholar 

  32. Shih, J.Y., Chen, Z.C., Chang, H.Y., Liu, Y.W., Ho, C.H., Chang, W.T.: Risks of age and sex on clinical outcomes post myocardial infarction. IJC Heart Vascul. 23, (2019)

    Article  Google Scholar 

  33. Liu, J.E., An, F.P.: Image classification algorithm based on deep learning-kernel function. Sci. Program. (2020)

    Google Scholar 

  34. Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., Chen, X.: A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 89, 171–178 (2016)

    Article  Google Scholar 

  35. Han, X., Zhong, Y., Zhao, B., Zhang, L.: Scene classification based on a hierarchical convolutional sparse auto-encoder for high spatial resolution imagery. Int. J. Remote Sens. 38(2), 514–536 (2017)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Maulana Abul Kalam Azad University of Technology, Kolkata, India

    Aritra Chakraborty, Santanu Chatterjee & Koushik Majumder

  2. Department of Electrical Electronics & Communication Engineering, Galgotias University, Noida, India

    Rabindra Nath Shaw

  3. School of Engineering and Applied Sciences, The Neotia University, Kolkata, West Bengal, India

    Ankush Ghosh

Authors
  1. Aritra Chakraborty
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  2. Santanu Chatterjee
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  3. Koushik Majumder
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  4. Rabindra Nath Shaw
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  5. Ankush Ghosh
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Corresponding author

Correspondence to Koushik Majumder .

Editor information

Editors and Affiliations

  1. Department of Information Engineering and Mathematics, University of Siena, Siena, Italy

    Monica Bianchini

  2. Department of Computer Science, University of Milan, Milan, Italy

    Vincenzo Piuri

  3. Regional Campus Manipur, Indira Gandhi National Tribal University, Imphal, Manipur, India

    Sanjoy Das

  4. School of Electrical and Electronic Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

    Rabindra Nath Shaw

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Chakraborty, A., Chatterjee, S., Majumder, K., Shaw, R.N., Ghosh, A. (2022). A Comparative Study of Myocardial Infarction Detection from ECG Data Using Machine Learning. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Networks and Systems, vol 218. Springer, Singapore. https://doi.org/10.1007/978-981-16-2164-2_21

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