Advertisement

Abstract

Cardiovascular disease (CVD) is one of the most serious diseases that harm human life and gives a huge burden to the health care system. Recent advances in deep learning have achieved great success in object detection, speech and image recognition. Although deep learning has been applied to the detection of arrhythmia, detection accuracy is limited because of three major issues: 1. Each ECG signal maybe contains more than one-label information; 2. It is hard to classify ECG with different lengths; 3. Data imbalance problem is severe for arrhythmia detection. In this paper, we present a multi-label learning algorithm to address the class imbalance and detection on ECGs with different durations. We utilize Deep Convolutional Generative Adversarial Networks (DCGANs) and Wasserstein GAN-Gradient Penalty (WGAN-GP) to generate new positive samples and use two losses to balance the importance between positive samples and negative samples. Moreover, we construct a Squeeze and Excitation-ResNet (SE-ResNet) module for normal rhythm and arrhythmia detection. In order to solve the multi-label classification problem, we train nine different binary classifiers for each category and determine which types of rhythm the ECG signals belong to. Experimental results on The ECG Intelligence Challenge 2019 dataset demonstrate that our multi-label learning method achieves competitive performance in multi-label ECGs classification.

Keywords

Arrhythmia ECG DCGANs WGAN-GP SE-ResNet Multi-label learning 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.61571628).

References

  1. 1.
    Thomas, H., Diamond, J., Vieco, A., et al.: Global atlas of cardiovascular disease 2000–2016: the path to prevention and control. Glob. Heart 13(3), 143 (2018)CrossRefGoogle Scholar
  2. 2.
    Mehra, R.: Global public health problem of sudden cardiac death. J. Electrocardiol. 40(6-supp-S1), S118–S122 (2007)CrossRefGoogle Scholar
  3. 3.
    Ye, C., Kumar, B.V.K.V., Coimbra, M.T.: Combining general multi-class and specific two-class classifiers for improved customized ECG heartbeat classification. In: 2012 21st International Conference on Pattern Recognition (ICPR 2012). IEEE Computer Society (2012)Google Scholar
  4. 4.
    Mar, T., Zaunseder, S., Martínez, J.P., Llamedo, M., Poll, R.: Optimization of ECG classification by means of feature selection. IEEE Trans. Bio-Med. Eng. 58(8), 2168–2177 (2011)CrossRefGoogle Scholar
  5. 5.
    Chazal, P.D., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196–1206 (2004)CrossRefGoogle Scholar
  6. 6.
    Moran, M.E., Soriano, M.C., Fischer, I., et al.: Electrocardiogram classification using reservoir computing with logistic regression. IEEE J. Biomed. Health Inform. 19(3) (2014)Google Scholar
  7. 7.
    Hong, S., Wu, M., et al.: ENCASE: an ensemble classifier for ECG classification using expert features and deep neural networks (2017)Google Scholar
  8. 8.
    Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., et al.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 25, 65–69 (2019)CrossRefGoogle Scholar
  9. 9.
    Zhang, Q., Zhou, D.: Deep Arm/Ear-ECG image learning for highly wearable biometric human identification. Ann. Biomed. Eng. 46(1), 1–13 (2017)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Zhang, M.-L., Zhou, Z.-H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)CrossRefGoogle Scholar
  11. 11.
    Poungponsri, S., Yu, X.H.: An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks. Neurocomputing 117, 206–213 (2013)CrossRefGoogle Scholar
  12. 12.
    Goodfellow, I.J., et al.: Generative adversarial nets. In: NIPS (2014)Google Scholar
  13. 13.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
  14. 14.
    Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein GANs. CoRR, abs/1704.00028 (2017)Google Scholar
  15. 15.
    Lin, T., Goyal, P., Girshick, R.B., He, K., Dollar, P.: Focal loss for dense object detection. In: IEEE ICCV (2017)Google Scholar
  16. 16.
    Li, B., Liu, Y., Wang, X.: Gradient harmonized single stage detector. In: AAAI Conference on Artificial Intelligence (2019)CrossRefGoogle Scholar
  17. 17.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  18. 18.
    Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: CVPR (2018)Google Scholar
  19. 19.
    Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: Proceedings of International Conference on Learning Representations, pp. 1–15 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jinjing Zhu
    • 1
  • Kaifa Xin
    • 1
  • Qingqing Zhao
    • 2
  • Yue Zhang
    • 1
    Email author
  1. 1.Division of Information Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Renmin Hospital of Wuhan UniversityWuhan UniversityWuhanChina

Personalised recommendations