Abstract
Visual inspection of electrocardiograms (ECGs) is a common clinical practice to diagnose heart diseases (HDs), which are still responsible for millions of deaths globally every year. In particular, myocardial infarction (MI) is the leading cause of mortality among HDs. ECGs reflect the electrical activity of the heart and provide a quicker process of diagnosis compared to laboratory blood tests. However, still it requires trained clinicians to interpret ECG waveforms, which poses a challenge in low-resourced healthcare systems, such as poor doctor-to-patient ratios. Previous works in this space have shown the use of data-driven approaches to predict HDs from ECG signals but focused on domain-specific features that are less generalizable across patient and device variations. Moreover, limited work has been conducted on the use of longitudinal information and fusion of multiple ECG leads. In contrast, we propose an end-to-end trainable solution for MI diagnosis, which (1) uses 12 ECG leads; (2) fuses the leads at data-level by stacking their spectrograms; (3) employs transfer learning to encode features rather than learning representations from scratch; and (4) uses a recurrent neural network to encode temporal dependency in long duration ECGs. Our approach is validated using multiple datasets, including tens of thousands of subjects, and encouraging performance is achieved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abubakar, S.M., Saadeh, W., Altaf, M.A.B.: A wearable long-term single-lead ECG processor for early detection of cardiac arrhythmia. In: 2018 Design, Automation and Test in Europe Conference and Exhibition (DATE), pp. 961–966. IEEE (2018)
Acharya, U.R., Fujita, H., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M.: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf. Sci. 415, 190–198 (2017)
Al Rahhal, M.M., Bazi, Y., AlHichri, H., Alajlan, N., Melgani, F., Yager, R.R.: Deep learning approach for active classification of electrocardiogram signals. Inf. Sci. 345, 340–354 (2016)
Ansari, S., et al.: 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 (2017)
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. Pattern Recogn. Lett. 122, 23–30 (2019)
Bax, J.J., et al.: Third universal definition of myocardial infarction. J. Am. Coll. Cardiol. 60(16), 1581–1598 (2012)
Bousseljot, R., Kreiseler, D., Schnabel, A.: Nutzung der ekg-signaldatenbank cardiodat der ptb über das internet. Biomedizinische Technik/Biomed. Eng. 40(s1), 317–318 (1995)
Darmawahyuni, A., et al.: Deep learning with a recurrent network structure in the sequence modeling of imbalanced data for ECG-rhythm classifier. Algorithms 12(6), 118 (2019)
Dash, S., Chon, K., Lu, S., Raeder, E.: Automatic real time detection of atrial fibrillation. Ann. Biomed. Eng. 37(9), 1701–1709 (2009)
Duong, H.T.H., et al.: Heart rate variability as an indicator of autonomic nervous system disturbance in tetanus. Am. J. Trop. Med. Hyg. 102(2), 403–407 (2020)
Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
Goldberger, A.L., Gold-berger, E.: Clinical electrocardiography, a simplified approach. Critical Care Med. 9(12), 891–892 (1981)
Han, C., Shi, L.: Ml-resnet: a novel network to detect and locate myocardial infarction using 12 leads ECG. Comput. Methods Programs Biomed. 185, 105138 (2020)
Kumar, M., Pachori, R., Acharya, U.: Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework. Entropy 19(9), 488 (2017)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Mehta, S., Lingayat, N., Sanghvi, S.: Detection and delineation of P and T waves in 12-lead electrocardiograms. Expert Syst. 26(1), 125–143 (2009)
Ravi, D., Wong, C., Lo, B., Yang, G.Z.: A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE J. Biomed. Health Inf. 21(1), 56–64 (2017)
Strodthoff, N., Strodthoff, C.: Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiol. Meas. (2018)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Tadesse, G.A., et al.: Multi-modal diagnosis of infectious diseases in the developing world. IEEE J. Biomed. Health Inf. (2020)
Tadesse, G.A., Javed, H., Weldemariam, K., Zhu, T.: A spectral-longitudinal model for detection of heart attack from12-lead electrocardiogram waveforms. In: Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) to appear (2020)
Tadesse, G.A., et al.: Cardiovascular disease diagnosis using cross-domain transfer learning. In: Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4262–4265 (2019)
Tadesse, G.A., Zhu, T., Thanh, N.L.N., Hung, N.T., Duong, H.T.H., Khanh, T.H., Quang, P.V., Tran, D.D., Yen, L.M., Doorn, H.R.V., andJohn Prince, N.V.H., Javed, H., Kiyasseh, D., Tan, L.V., Thwaites, L., Clifton, D.A.: Severity detection tool for patients with infectious disease. arXiv preprint arXiv:1912.05345 (2019)
WHO: Cardiovascular diseases (CVDs). www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed 13 Aug 2020
Acknowledgements
This project was supported by the EPSRC “FAST" Healthcare NetworkPlus initiative. TZ was supported by the RAEng Engineering for Development Research Fellowship.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Tadesse, G.A. et al. (2021). Discriminant Knowledge Extraction from Electrocardiograms for Automated Diagnosis of Myocardial Infarction. In: Uehara, H., Yamaguchi, T., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2021. Lecture Notes in Computer Science(), vol 12280. Springer, Cham. https://doi.org/10.1007/978-3-030-69886-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-69886-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69885-0
Online ISBN: 978-3-030-69886-7
eBook Packages: Computer ScienceComputer Science (R0)