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Classification of normal and abnormal ECG records using lead convolutional neural network and rule inference

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References

  1. 1

    Dong J, Zhang J W, Zhu H H, et al. Wearable ECG monitors and its remote diagnosis service platform. IEEE Intell Syst, 2012, 6: 36–43

  2. 2

    Ye C, Kumar B V, Coimbra M T. Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans Biomed Eng, 2012, 59: 2930–2941

  3. 3

    Willems J L, Abreu-Lima C, Arnaud P, et al. The diagnostic performance of computer programs for the interpretation of electrocardiograms. New England J Medicine, 1991, 325: 1767–1773

  4. 4

    Zhang J W, Liu X, Dong J. CCDD: an enhanced standard ECG database with its management & annotation tools. Int J Artif Intell Tools, 2012, 21: 1–26

  5. 5

    Zhu H H. Research on ECG recognition critical methods and development on remote multi body characteristic signal monitoring system. Dissertation for Ph.D. Degree. Beijing: University of Chinese Academy of Sciences, 2013

  6. 6

    Wang L P. Study on approach of ECG classification with domain knowledge. Dissertation for Ph.D. Degree. Shanghai: East China Normal University, 2013

  7. 7

    Jin L P, Dong J. Deep learning research on clinical electrocardiogram analysis (in Chinese). Sci Sin Inform, 2015, 45: 398–415

  8. 8

    Zhu H H, Dong J. An R-peak detection method based on peaks of Shannon energy envelope. Biomed Signal Process Control, 2013, 8: 466–474

  9. 9

    Jin L P, Dong J. Ensemble deep learning for biomedical time series classification. Comput Intell Neurosci, 2016, 2016: 6212684

  10. 10

    Liu X. Atlas of Classical Electrocardiograms. Shanghai: Shanghai Science and Technology Press, 2011

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Correspondence to Jun Dong.

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The authors declare that they have no conflict of interest.

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Jin, L., Dong, J. Classification of normal and abnormal ECG records using lead convolutional neural network and rule inference. Sci. China Inf. Sci. 60, 078103 (2017). https://doi.org/10.1007/s11432-016-9047-6

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