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Cardiovascular diseases

Artificial intelligence for the electrocardiogram

Deep-learning algorithms can be applied to large datasets of electrocardiograms, are capable of identifying abnormal heart rhythms and mechanical dysfunction, and could aid healthcare decisions.

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Fig. 1: A deep neural network approach for analyzing electrocardiograms.

References

  1. Bycroft, C. et al. Nature 562, 203–209 (2018).

    Article  CAS  Google Scholar 

  2. Hannun, A. Y. et al. Nat. Med. https://doi.org/10.1038/s41591-018-0268-3 (2019).

  3. Attia, Z. I. et al. Nat. Med. https://doi.org/10.1038/s41591-018-0240-2 (2019).

  4. Lau, J. K. et al. Int. J. Cardiol. 165, 193–194 (2013).

    Article  Google Scholar 

  5. Lyon, A., Mincholé, A., Martínez, J. P., Laguna, P. & Rodriguez, B. J. R. Soc. Interface 15, 20170821 (2018).

  6. Kiranyaz, S., Ince, T. & Gabbouj, M. IEEE Trans. Biomed. Eng. 63, 664–675 (2016).

    Article  Google Scholar 

  7. Moody, G. B. & Mark, R. G. IEEE Eng. Med. Biol. Mag. 20, 45–50 (2001).

    Article  CAS  Google Scholar 

  8. Lyon, A., Bueno-Orovio, A., Zacur, E., Ariga, R., Grau, V., Neubauer, S., Watkins, H., Rodriguez, B. & Mincholé, A. Europace 20, iii102–iii112 (2018).

    Article  Google Scholar 

  9. Martínez, J. P., Almeida, R., Olmos, S., Rocha, A. P. & Laguna, P. IEEE Trans. Biomed. Eng. 51, 570–581 (2004).

    Article  Google Scholar 

  10. Camps, J., McCarthy, A., Rodrıguez, B. & Minchole, A. Deep learning based QRS multilead delineator in electrocardiogram signals. In Proc. 3rd International Workshop on Biomedical Informatics with Optimization and Machine Learning (2018).

  11. Bai, W. et al. J. Cardiovasc. Magn. Reson. 20, 65 (2018).

    Article  Google Scholar 

  12. Goldberger, A. L. et al. Circulation 101, E215–E220 (2000).

Download references

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Correspondence to Blanca Rodriguez.

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Mincholé, A., Rodriguez, B. Artificial intelligence for the electrocardiogram. Nat Med 25, 22–23 (2019). https://doi.org/10.1038/s41591-018-0306-1

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