Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Oct 2;402(8):911-923.
doi: 10.1515/hsz-2020-0169. Print 2021 Jul 27.

Detecting myocardial scar using electrocardiogram data and deep neural networks

Nils Gumpfer  1 , Dimitri Grün  2 , Jennifer Hannig  1 , Till Keller  2 , Michael Guckert  1   3
Affiliations
Free article

Detecting myocardial scar using electrocardiogram data and deep neural networks

Nils Gumpfer et al. Biol Chem. .
Free article

Abstract

Ischaemic heart disease is among the most frequent causes of death. Early detection of myocardial pathologies can increase the benefit of therapy and reduce the number of lethal cases. Presence of myocardial scar is an indicator for developing ischaemic heart disease and can be detected with high diagnostic precision by magnetic resonance imaging. However, magnetic resonance imaging scanners are expensive and of limited availability. It is known that presence of myocardial scar has an impact on the well-established, reasonably low cost, and almost ubiquitously available electrocardiogram. However, this impact is non-specific and often hard to detect by a physician. We present an artificial intelligence based approach - namely a deep learning model - for the prediction of myocardial scar based on an electrocardiogram and additional clinical parameters. The model was trained and evaluated by applying 6-fold cross-validation to a dataset of 12-lead electrocardiogram time series together with clinical parameters. The proposed model for predicting the presence of scar tissue achieved an area under the curve score, sensitivity, specificity, and accuracy of 0.89, 70.0, 84.3, and 78.0%, respectively. This promisingly high diagnostic precision of our electrocardiogram-based deep learning models for myocardial scar detection may support a novel, comprehensible screening method.

Keywords: ECG classification; artificial intelligence; convolutional neural networks; deep learning; ischaemic heart disease; myocardial scar.

PubMed Disclaimer

References

    1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2015). TensorFlow: large-scale machine learning on heterogeneous systems, Software available from tensorflow.org. http://tensorflow.org/ (visited on 03/26/2020).
    1. Acharya, U.R., Fujita, H., Oh, S.L., Hagiwara, Y., Tan, J.H., and Adam, M. (2017). 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.
    1. Albuquerque, V.H.C., Nunes, T.M., Pereira, D.R., Luz, E.J.D.S., Menotti, D., Papa, J.P., and Tavares, J.M.R.S. (2018). Robust automated cardiac arrhythmia detection in ECG beat signals. Neural Comput. Appl. 29: 679–693, https://doi.org/10.1007/s00521-016-2472-8.
    1. Altman, D.G. and Royston, P. (2000). What do we mean by validating a prognostic model?. Stat. Med. 19: 453–473, https://doi.org/10.1002/(sici)1097-0258(20000229)19:4<453::aid-sim350....
    1. Andras, P., Esterle, L., Guckert, M., Han, T.A., Lewis, P.R., Milanovic, K., Payne, T., Perret, C., Pitt, J., Powers, S.T., et al. (2018). Trusting intelligent machines: deepening trust within socio-technical systems. IEEE Technol. Soc. Mag. 37: 76–83, https://doi.org/10.1109/mts.2018.2876107.

LinkOut - more resources