Abstract
Myocardial infarction is the leading cause of death worldwide. In this paper, we design domain-inspired neural network models to detect myocardial infarction. First, we study the contribution of various leads. This systematic analysis, first of its kind in the literature, indicates that out of 15 ECG leads, data from the v6, vz, and ii leads are critical to correctly identify myocardial infarction. Second, we use this finding and adapt the ConvNetQuake neural network model—originally designed to identify earthquakes—to attain state-of-the-art classification results for myocardial infarction, achieving 99.43% classification accuracy on a record-wise split, and 97.83% classification accuracy on a patient-wise split. These two results represent cardiologist-level performance level for myocardial infarction detection after feeding only 10 s of raw ECG data into our model. Third, we show that our multi-ECG-channel neural network achieves cardiologist-level performance without the need of any kind of manual feature extraction or data pre-processing.
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Notes
- 1.
A poor train-val-test split refers to one where the distribution of the train data does not match the distribution of the test data. With such a small dataset, this occurs at a relatively high frequency when the data is split up randomly.
References
Acharya, U.R., et al.: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf. Sci. 415, 190–198 (2017). https://doi.org/10.1016/j.ins.2017.06.027
Ackermann, S.: Using transfer learning to detect galaxy mergers. Mon. Notices Roy. Astron. Soc. 479(1), 415–425 (2018). https://doi.org/10.1093/mnras/sty1398
Gupta, A.: Myocardial Infarction Detection. https://github.com/arjung128/mi_detection
Berg, J., Nyström, K.: A unified deep artificial neural network approach to partial differential equations in complex geometries. Neurocomputing 317, 28–41 (2018)
Bhattacharya, T., et al.: AI meets exascale computing: Advancing cancer research with large-scale high performance computing. Front. Oncol. 9, 984 (2019). ISSN 2234-943X. https://doi.org/10.3389/fonc.2019.00984, https://www.frontiersin.org/article/10.3389/fonc.2019.00984
Choma, N., Monti, F., et al.: Graph neural networks for icecube signal classification. In: Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, vol. 1, pp. 386–391. Institute of Electrical and Electronics Engineers Inc. (2019). https://doi.org/10.1109/ICMLA.2018.00064
Chua, A.J., Galley, C.R., Vallisneri, M.: Reduced-order modeling with artificial neurons for gravitational-wave inference. Phys. Rev. Lett. 122(21), 211101 (2019). https://doi.org/10.1103/PhysRevLett.122.211101
Dominguez Sánchez, H., et al.: Improving galaxy morphologies for SDSS with deep learning. Mon. Notices Roy. Astron. Soc. 476, 3661–3676 (2018). https://doi.org/10.1093/mnras/sty338
Dominguez Sánchez, H., et al.: Transfer learning for galaxy morphology from one survey to another. Mon. Notices Roy. Astron. Soc. 484(1), 93–100 (2019). https://doi.org/10.1093/mnras/sty3497
Feng, K., et al.: Myocardial infarction classification based on convolutional neural network and recurrent neural network. Appl. Sci. (2019). https://doi.org/10.3390/app9091879
George, D., Huerta, E.A.: Deep Learning for real-time gravitational wave detection and parameter estimation: Results with AdvancedLIGO data. Phys. Lett. B 778, 64–70 (2018). https://doi.org/10.1016/j.physletb.2017.12.053. arXiv: 1711.03121 [gr-qc]
George, D., Huerta, E.A.: Deep neural networks to enable real-time multimessenger astrophysics. Phys. Rev. D 778, 97–044039 (2018). https://doi.org/10.1103/PhysRevD.97.044039. arXiv: 1701.00008 [astro-ph.IM]
George, D., Shen, H., Huerta, E.A.: Classification and unsupervised clustering of LIGO data with deep transfer learning. Phys. Rev. D 97, 101501 (2018). https://doi.org/10.1103/PhysRevD.97.101501. arXiv: 1706.07446 [gr-qc]
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)
Huerta, E.A., et al.: Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure, March 2020. arXiv e-prints, arXiv:2003.08394 [physics.comp-ph]
Huerta, E.A., et al.: Enabling real-time multi-messenger astrophysics discoveries with deep learning. Nat. Rev. Phys. 1(10), 600–608 (2019). https://doi.org/10.1038/s42254-019-0097-4. arXiv:1911.11779 [gr-qc]
Fawaz, H.I., et al.: Deep learning for time series classification: a review. Data Mining Knowl. Disc. 33(4), 917–963 (2019). https://doi.org/10.1007/s10618-019-00619-1. ISSN 1573-756X
Kachuee, M., Fazeli, S., Sarrafzadeh, M.: ECG heartbeat classification: a deep transferable representation. In: IEEE Conference on Healthcare Informatics, July 2018. arXiv: 1805.00794
Asad Khan, E.A., et al.: Deep learning at scale for the construction of galaxy catalogs in the dark energy survey. Phys. Lett. B 795, 248 – 258 (2019). ISSN 0370-2693. https://doi.org/10.1016/j.physletb.2019.06.009. https://www.sciencedirect.com/science/article/pii/S0370269319303879
Kojuri, J., et al.: Prediction of acute myocardial infarction with artificial neural networks in patients with non-diagnostic electrocardiogram. J. Cardiovasc. Dis. Res. (2015). https://doi.org/10.5530/jcdr.2015.2.2
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539
Liu, B., et al.: A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Comput. Biol. Med. (2015). https://doi.org/10.1016/j.compbiomed.2014.08.010
Liu, N., et al.: A simple and effective method for detecting myocardial infarction based on deep convolutional neural network. J. Med. Imaging Health Inform. (2018). https://doi.org/10.1166/jmihi.2018.2463
T. Perol, M. Gharbi, and M. Denolle. Convolutional neural network for earthquake detection and location.Sci. Adv. 4, e1700578 (2018). https://doi.org/10.1126/sciadv.1700578, pmid: 29487899
Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686 – 707 (2019). ISSN 0021-9991. https://doi.org/10.1016/j.jcp.2018.10.045. https://www.sciencedirect.com/science/article/pii/S0021999118307125
Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics informed deep learning (part I): data-driven solutions of nonlinear partial differential equations. CoRR, abs/1711.10561 (2017). https://arxiv.org/abs/1711.10561
Reasat, T., Shahnaz, C.: Detection of inferior myocardial infarction using shallow convolutional neural networks. In: IEEE Region 10 Humanitarian Technology Conference, August 2018. https://doi.org/10.1109/R10-HTC.2017.8289058
Rebei, A., et al.: Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers. Phys. Rev. D 100(4), 044025 (2019). https://doi.org/10.1103/PhysRevD.100.044025
Remya, R.S., Indiradevi, K.P., Anish Babu, K.K.: Classification of myocardial infarction using multi resolution wavelet analysis of ECG. In: International Conference on Emerging Trends in Engr., Science and Technology, December 2016. https://doi.org/10.1016/j.protcy.2016.05.195
Safdarian, N., Dabanloo, N.J., 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). https://doi.org/10.4236/jbise.2014.710081
Schmidhuber., J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85 – 117 (2015). ISSN 0893-6080. https://doi.org/10.1016/j.neunet.2014.09.003. https://www.sciencedirect.com/science/article/pii/S0893608014002135
Sharma, L.D., Sunkaria, R.K.: Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal Image Video Process. (2017). https://doi.org/10.1007/s11760-017-1146-z
Shen, H., et al.: Denoising gravitational waves with enhanced deep recurrent denoising auto-encoders, pp. 3237–3241, May 2019. https://doi.org/10.1109/ICASSP.2019.8683061
Shen, H., Huerta, E.A., Zhao, Z.: Deterministic and Bayesian Neural Networks for Low-latency Gravitational Wave Parameter Estimation of Binary Black Hole Mergers (2019). arXiv: 1903.01998 [gr-qc]
Springenberg, J.T., et al.: Bayesian optimization with robust bayesian neural networks, pp. 4134–4142. Curran Associates, Inc. (2016)
Sun, L., et al.: ECG analysis using multiple instance learning for myocardial infarction detection. IEEE Trans. Biomed. Eng. (2012). https://doi.org/10.1109/TBME.2012.2213597
Tang, H., Scaife, A.M.M., Leahy, J.P.: Transfer learning for radio galaxy classification. Mon. Notices Roy. Astron. Soc. 488(3), 3358–3375 (2019). https://doi.org/10.1093/mnras/stz1883
United States Department of Energy. Scientific Machine Learning. https://www.osti.gov/servlets/purl/1478744
Wei, W., Huerta, E.A.: Gravitational wave denoising of binary black hole mergers with deep learning. Phys. Lett. B 800, 135081 (2020). ISSN 0370-2693. https://doi.org/10.1016/j.physletb.2019.135081. https://www.sciencedirect.com/science/article/pii/S0370269319308032
Wei, W., et al.: Deep transfer learning for star cluster classification: I. application to the PHANGS-HST survey. Mon. Notices Roy. Astron. Soc. 493(3), 3178–3193 (2020). https://doi.org/10.1093/mnras/staa325
Zewdie, G., Xiong, M.: Fully automated myocardial infarction classification using ordinary differential equations. arXiv e-prints, arXiv:1410.6984, October 2014
Acknowledgments
EAH and ZZ gratefully acknowledge National Science Foundation (NSF) awards OAC-1931561 and OAC-1934757. AG acknowledges support from the Fiddler Innovation Undergraduate Fellowship and the Students Pushing Innovation (SPIN) program at the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign.
This work utilized resources supported by the NSF’s Major Research Instrumentation program, grant OAC-1725729, as well as the University of Illinois at Urbana-Champaign. We are grateful to NVIDIA for donating several Tesla P100 and V100 GPUs that we used for our analysis, and the NSF grants NSF-1550514, NSF-1659702 and TG-PHY160053. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. We thank the NCSA Gravity Group for useful feedback.
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Gupta, A., Huerta, E., Zhao, Z., Moussa, I. (2021). Deep Learning for Cardiologist-Level Myocardial Infarction Detection in Electrocardiograms. In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds) 8th European Medical and Biological Engineering Conference. EMBEC 2020. IFMBE Proceedings, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-64610-3_40
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