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Artificial intelligence-based myocardial infarction diagnosis: a comprehensive review of modern techniques

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Abstract

Myocardial infarction (MI), commonly known as a heart attack, is a serious medical condition that can lead to congestive heart failure and even death. Prompt diagnosis and early intervention are essential for improving a patient’s survival chances. Electrocardiography (ECG) is the most commonly used diagnostic method for MI, but other noninvasive imaging techniques and clinical parameters are also used. However, manual interpretation of these methods can result in potential inconsistencies among different observers. To address this issue, automated computer-aided diagnostic systems that utilize artificial intelligence (AI) have been developed. These systems use both machine learning (ML) and deep learning (DL) models to discriminate between MI and normal signals or subjects. In this review paper, we survey the current state-of-the-art methods in ML and DL-based MI detection approaches that are published from 2015 to the present. This review highlights the advantages and limitations of different AI-based approaches and provides insights into future directions for research in this field. The ultimate goal of these efforts is to improve the accuracy and efficiency of MI diagnosis and contribute to more efficient and timely diagnosis of MI patients.

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References

  1. Canto JG, Shlipak MG, Rogers WJ, Malmgren JA, Frederick PD, Lambrew CT, Ornato JP, Barron HV, Kiefe CI (2000) Prevalence, clinical characteristics, and mortality among patients with myocardial infarction presenting without chest pain. Jama 283(24):3223–3229

    Google Scholar 

  2. Reed GW, Rossi JE, Cannon CP (2017) Acute myocardial infarction. The Lancet 389(10065):197–210

    Google Scholar 

  3. Jayachandran E, Joseph KP, Acharya UR (2010) Analysis of myocardial infarction using discrete wavelet transform. J Med Syst 34:985–992

    Google Scholar 

  4. Creemers EE, Cleutjens JP, Smits JF, Daemen MJ (2001) Matrix metalloproteinase inhibition after myocardial infarction: a new approach to prevent heart failure? Circ Res 89(3):201–210

    Google Scholar 

  5. Banerjee S, Mitra M (2012) Cross wavelet transform based analysis of electrocardiogram signals. International Journal of Electrical, Electronics and Computer Engineering 1(2):88–92

    Google Scholar 

  6. Liu B, Liu J, Wang G, Huang K, Li F, Zheng Y, Luo Y, Zhou F (2015) A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Comput Biol Med 61:178–184

    Google Scholar 

  7. Hammad M, Kandala RN, Abdelatey A, Abdar M, Zomorodi-Moghadam M, San Tan R, Acharya UR, Pławiak J, Tadeusiewicz R, Makarenkov V et al (2021) Automated detection of shockable ecg signals: a review. Inf Sci 571:580–604

    MathSciNet  Google Scholar 

  8. Bousseljot R, Kreiseler D, Schnabel A (1995) Nutzung der ekg-signaldatenbank cardiodat der ptb über das internet

  9. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology 2(4)

  10. Yu K-H, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nature biomedical engineering 2(10):719–731

    Google Scholar 

  11. Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Future healthcare journal 6(2):94

    Google Scholar 

  12. Ribeiro JM, Astudillo P, de Backer O, Budde R, Nuis RJ, Goudzwaard J, Van Mieghem NM, Lumens J, Mortier P, Mattace-Raso F et al (2022) Artificial intelligence and transcatheter interventions for structural heart disease: a glance at the (near) future. Trends in cardiovascular medicine 32(3):153–159

    Google Scholar 

  13. Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255–260

    MathSciNet  Google Scholar 

  14. Alharthi AS, Yunas SU, Ozanyan KB (2019) Deep learning for monitoring of human gait: a review. IEEE Sensors J 19(21):9575–9591

    Google Scholar 

  15. Hellermann JP, Jacobsen SJ, Gersh BJ, Rodeheffer RJ, Reeder GS et al (2002) Heart failure after myocardial infarction: a review. The American journal of medicine 113(4):324–330

    Google Scholar 

  16. Sandoval Y, Jaffe AS (2019) Type 2 myocardial infarction: Jacc review topic of the week. J Am Coll Cardiol 73(14):1846–1860

    Google Scholar 

  17. Lewandrowski K, Chen A, Januzzi J (2002) Cardiac markers for myocardial infarction: a brief review. Pathology Patterns Reviews 118(suppl_1):S93–S99

  18. Hankins G, Wendel GD Jr, Leveno KJ, Stoneham J (1985) Myocardial infarction during pregnancy: a review. Obstet Gynecol 65(1):139–146

    Google Scholar 

  19. Braunwald E (2012) The treatment of acute myocardial infarction: the past, the present, and the future. European Heart Journal: Acute Cardiovascular Care 1(1):9–12

    Google Scholar 

  20. Sharma L, Tripathy R, Dandapat S (2015) Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans Biomed Eng 62(7):1827–1837

    Google Scholar 

  21. Kumar M, Pachori RB, Acharya UR (2017) Automated diagnosis of myocardial infarction ecg signals using sample entropy in flexible analytic wavelet transform framework. Entropy 19(9):488

    Google Scholar 

  22. Diker A, Cömert Z, Avci E, Velappan S (2018) Intelligent system based on genetic algorithm and support vector machine for detection of myocardial infarction from ecg signals. In 2018 26th Signal processing and communications applications conference (SIU). Ieee, pp 1–4

  23. Han C, Shi L (2019) Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features. Comput Methods Prog Biomed 175:9–23

    Google Scholar 

  24. Valizadeh G, Babapour Mofrad F, Shalbaf A (2021) Parametric-based feature selection via spherical harmonic coefficients for the left ventricle myocardial infarction screening. Med Biol Eng Comput 59(6):1261–1283

    Google Scholar 

  25. Attallah O, Ragab DA (2023) Auto-myin: automatic diagnosis of myocardial infarction via multiple glcms, cnns, and svms. Biomedical Signal Processing and Control 80:104273

    Google Scholar 

  26. Valizadeh G, Mofrad FB (2023) Parametrized pre-trained network (ppnet): a novel shape classification method using spharms for mi detection. Expert Syst Appl 228:120368

    Google Scholar 

  27. Acharya UR, Fujita H, Sudarshan VK, Oh SL, Adam M, Koh JE, Tan JH, Ghista DN, Martis RJ, Chua CK et al (2016) Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl-Based Syst 99:146–156

    Google Scholar 

  28. Acharya UR, Fujita H, Adam M, Lih OS, Sudarshan VK, Hong TJ, Koh JE, Hagiwara Y, Chua CK, Poo CK et al (2017) Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ecg signals: a comparative study. Inf Sci 377:17–29

    Google Scholar 

  29. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. circulation 101(23):e215–e220

    Google Scholar 

  30. Iyengar N, Peng C, Morin R, Goldberger AL, Lipsitz LA (1996) Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 271(4):R1078–R1084

    Google Scholar 

  31. Zhang J, Lin F, Xiong P, Du H, Zhang H, Liu M, Hou Z, Liu X (2019) Automated detection and localization of myocardial infarction with staked sparse autoencoder and treebagger. IEEE Access 7:70 634–70 642

  32. 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. SIViP 14:857–865

    Google Scholar 

  33. Kayikcioglu I, Akdeniz F, Köse C, Kayikcioglu T (2020) Time-frequency approach to ecg classification of myocardial infarction. Comput Electr Eng 84:106621

    Google Scholar 

  34. Mohd Faizal AS, Hon WY, Thevarajah TM, Khor SM, Chang S-W (2023) A biomarker discovery of acute myocardial infarction using feature selection and machine learning. Med Biol Eng Comput, pp 1–15

  35. Reasat T, Shahnaz C (2017) Detection of inferior myocardial infarction using shallow convolutional neural networks. In 2017 IEEE region 10 humanitarian technology conference (R10-HTC). IEEE, pp 718–721

  36. Gupta A, Huerta E, Zhao Z, Moussa I (2021) Deep learning for cardiologist-level myocardial infarction detection in electrocardiograms. In 8th European medical and biological engineering conference: proceedings of the EMBEC 2020, November 29–December 3, 2020 Portorož, Slovenia. Springer, pp 341–355

  37. Tripathy RK, Bhattacharyya A, Pachori RB (2019) A novel approach for detection of myocardial infarction from ecg signals of multiple electrodes. IEEE Sensors J 19(12):4509–4517

    Google Scholar 

  38. Zhang G, Si Y, Wang D, Yang W, Sun Y (2019) Automated detection of myocardial infarction using a gramian angular field and principal component analysis network. IEEE Access 7:171 570–171 583

  39. 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):1879

    Google Scholar 

  40. Liu W, Wang F, Huang Q, Chang S, Wang H, He J (2019) Mfb-cbrnn: a hybrid network for mi detection using 12-lead ecgs. IEEE journal of biomedical and health informatics 24(2):503–514

    Google Scholar 

  41. Han C, Shi L (2020) Ml-resnet: a novel network to detect and locate myocardial infarction using 12 leads ecg. Comput Methods Prog Biomed 185:105138

    Google Scholar 

  42. Fu L, Lu B, Nie B, Peng Z, Liu H, Pi X (2020) Hybrid network with attention mechanism for detection and location of myocardial infarction based on 12-lead electrocardiogram signals. Sensors 20(4):1020

    Google Scholar 

  43. Jahmunah V, Ng EYK, San TR, Acharya UR (2021) Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using gaborcnn model with ecg signals. Comput Biol Med 134:104457

    Google Scholar 

  44. Kim Y-C, Kim KR, Choe YH (2020) Automatic myocardial segmentation in dynamic contrast enhanced perfusion mri using monte carlo dropout in an encoder-decoder convolutional neural network. Comput Methods Prog Biomed 185:105150

    Google Scholar 

  45. Garland J, Hu M, Duffy M, Kesha K, Glenn C, Morrow P, Stables S, Ondruschka B, Da Broi U, Tse RD (2021) Classifying microscopic acute and old myocardial infarction using convolutional neural networks. The American Journal of Forensic Medicine and Pathology 42(3):230–234

    Google Scholar 

  46. Degerli A, Zabihi M, Kiranyaz S, Hamid T, Mazhar R, Hamila R, Gabbouj M (2021) Early detection of myocardial infarction in low-quality echocardiography. IEEE Access 9:34 442–34 453

  47. Guo Y, Du G-Q, Shen W-Q, Du C, He P-N, Siuly S (2021) Automatic myocardial infarction detection in contrast echocardiography based on polar residual network. Comput Methods Prog Biomed 198:105791

    Google Scholar 

  48. Hammad M, Alkinani MH, Gupta B, Abd El-Latif AA (2021) Myocardial infarction detection based on deep neural network on imbalanced data. Multimedia Systems, pp 1–13

  49. Alghamdi A, Hammad M, Ugail H, Abdel-Raheem A, Muhammad K, Khalifa HS, Abd El-Latif AA (2020) Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. Multimedia tools and applications, pp 1–22

  50. Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M (2017) Application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals. Inf Sci 415:190–198

    Google Scholar 

  51. Liu W, Zhang M, Zhang Y, Liao Y, Huang Q, Chang S, Wang H, He J (2017) Real-time multilead convolutional neural network for myocardial infarction detection. IEEE journal of biomedical and health informatics 22(5):1434–1444

    Google Scholar 

  52. Mofrad FB, Valizadeh G (2023) Densenet-based transfer learning for lv shape classification: introducing a novel information fusion and data augmentation using statistical shape/color modeling. Expert Syst Appl 213:119261

    Google Scholar 

  53. Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng P-A, Cetin I, Lekadir K, Camara O, Ballester MAG et al (2018) Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37(11):2514–2525

    Google Scholar 

  54. Scheffler K, Lehnhardt S (2003) Principles and applications of balanced ssfp techniques. Eur Radiol 13:2409–2418

    Google Scholar 

  55. Lalande A, Chen Z, Decourselle T, Qayyum A, Pommier T, Lorgis L, de La Rosa E, Cochet A, Cottin Y, Ginhac D et al (2020) Emidec: a database usable for the automatic evaluation of myocardial infarction from delayed-enhancement cardiac mri. Data 5(4):89

    Google Scholar 

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“This research did not receive any external funding.”

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Authors and Affiliations

  1. Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan, 64200, Punjab, Pakistan

    Hafeez Ur Rehman Siddiqui, Kainat Zafar, Adil Ali Saleem & Rukhshanda Sehar

  2. School of Computer Science, University College Dublin, D04 V1W8, Dublin, Ireland

    Furqan Rustam

  3. Bioengineering Research Centre, School of Engineering, London South Bank University, 103 Borough Road, London, SE1 0AA, UK

    Sandra Dudley

  4. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea

    Imran Ashraf

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  1. Hafeez Ur Rehman Siddiqui
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  2. Kainat Zafar
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  4. Rukhshanda Sehar
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Correspondence to Imran Ashraf.

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Siddiqui, H.U.R., Zafar, K., Saleem, A.A. et al. Artificial intelligence-based myocardial infarction diagnosis: a comprehensive review of modern techniques. Multimed Tools Appl 83, 41951–41979 (2024). https://doi.org/10.1007/s11042-023-17246-0

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