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Automatic Detection of ECG Abnormalities by Using an Ensemble of Deep Residual Networks with Attention

  • Yang Liu
  • Runnan He
  • Kuanquan Wang
  • Qince Li
  • Qiang Sun
  • Na Zhao
  • Henggui ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11794)

Abstract

Heart disease is one of the most common diseases causing morbidity and mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart diseases for its simplicity and non-invasive property. Automatic ECG analyzing technologies are expected to reduce human working load and increase diagnostic efficacy. However, there are still some challenges to be addressed for achieving this goal. In this study, we develop an algorithm to identify multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline, several preprocessing methods are firstly applied on the ECG data for denoising, augmentation and balancing recording numbers of variant classes. In consideration of efficiency and consistency of data length, the recordings are padded or truncated into a medium length, where the padding/truncating time windows are selected randomly to suppress overfitting. Then, the ECGs are used to train deep neural network (DNN) models with a novel structure that combines a deep residual network with an attention mechanism. Finally, an ensemble model is built based on these trained models to make predictions on the test data set. Our method is evaluated based on the test set of the First China ECG Intelligent Competition dataset by using the F1 metric that is regarded as the harmonic mean between the precision and recall. The resultant overall F1 score of the algorithm is 0.875, showing a promising performance and potential for practical use.

Keywords

Heart disease Electrocardiogram Automatic diagnosis Deep neural networks 

Notes

Acknowledgements

The work is supported by the National Science Foundation of China (NSFC) under Grant Nos. 61572152 (to HZ), 61571165 (to KW), 61601143 (to QL) and 81770328 (to QL), the Science Technology and Innovation Commission of Shenzhen Municipality under Grant Nos. JSGG20160229125049615 and JCYJ20151029173639477 (to HZ), and China Postdoctoral Science Foundation under Grant Nos. 2015M581448 (to QL).

References

  1. 1.
    Ari, S., Das, M.K., Chacko, A.: ECG signal enhancement using S-transform. IEEE Trans. Biomed. Eng. 43(6), 649–660 (2013)Google Scholar
  2. 2.
    De Chazal, P., Reilly, R.B.: A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 53(12), 2535–2543 (2006)CrossRefGoogle Scholar
  3. 3.
    Yang, H., Kan, C., Liu, G., Chen, Y.: Spatiotemporal differentiation of myocardial infarctions. IEEE Trans. Autom. Sci. Eng. 10(4), 938–947 (2013)CrossRefGoogle Scholar
  4. 4.
    Dima, S.-M., et al.: On the detection of myocadial scar based on ECG/VCG analysis. IEEE Trans. Biomed. Eng. 60(12), 3399–3409 (2013)CrossRefGoogle Scholar
  5. 5.
    Yu, S.N., Chou, K.T.: Integration of independent component analysis and neural networks for ECG beat classification. Expert Syst. Appl. 34, 2841–2846 (2008)CrossRefGoogle Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. IEEE, Las Vegas, NV, USA (2016)Google Scholar
  7. 7.
    He, R., et al.: Automatic detection of atrial fibrillation based on continuous wavelet transform and 2D convolutional neural networks. Front. Physiol. 9, 1206 (2018)CrossRefGoogle Scholar
  8. 8.
    Oh, S.L., Ng, E.Y., San Tan, R., Acharya, U.R.: Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput. Biol. Med. 102, 278–287 (2018)CrossRefGoogle Scholar
  9. 9.
    Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inform. 41(3), 613–627 (1995)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yang Liu
    • 1
  • Runnan He
    • 1
  • Kuanquan Wang
    • 1
  • Qince Li
    • 1
  • Qiang Sun
    • 5
  • Na Zhao
    • 1
  • Henggui Zhang
    • 1
    • 2
    • 3
    • 4
    Email author
  1. 1.School of Computer Science and TechnologyHarbin Institute of Technology (HIT)HarbinChina
  2. 2.School of Physics and AstronomyThe University of ManchesterManchesterUK
  3. 3.SPACEnter Space Science and Technology InstituteShenzhenChina
  4. 4.International Laboratory for Smart Systems and Key Laboratory of Intelligent of Computing in Medical Image, Ministry of EducationNortheastern UniversityShenyangChina
  5. 5.The Department of PharmacologyBeijing Electric Power HospitalBeijingChina

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