Adaptive Latent Space Domain Transfer for Atrial Fibrillation Detection

  • Xing-Bin Qin
  • Yan Yan
  • Jianping Fan
  • Lei WangEmail author
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 64)


Atrial fibrillation (AF) is characterised by disorganised atrial electrical activity and contraction. The complications of atrial fibrillation is varied, so it shows several modalities in the Electrocardiogram (ECG). According to our statistics, the beat features and duration time of AF are different from person to person. The distribution of available annotated source ECG date is not as the target. Due to the rapid development of portable monitor, the ECG data explodes. The existing algorithms for the detection of AF perform well only in the training database. Training a general used model requires a great deal of labeled data for every user, this is a huge amount of work that is almost impossible. In order to make full use of the limited labeled ECG data and training a general model, we propose an adaptive latent space domain transfer method for the detection of AF. The model learned from the source data is automatically adapted with little annotated or none in the target data. The MIT-BIH Atrial Fibrillation Database is regard as standard reference for classifier. Then we carry the experimental verification on the MIT-BIH Normal Sinus Rhythm Database and the Long-Term AF Database. The transfer method shows better performance than directly applied. It does make sense for detection and analysis of clinical dynamic electrocardiogram and individual ECG monitoring.



This study was financed partially by the National 863 Program of China (Grant No. 2012AA02A604), the Next generation communication technology Major project of National S&T (Grant No. 2013ZX03005013), the Key Research Program of the Chinese Academy of Sciences, and the Guangdong Innovation Research Team Funds for Image-Guided Therapy and Low-cost Healthcare.


  1. 1.
    M. Stridh and L. Sornmo, “Spatiotemporal QRST Cancellation Techniques for Analysis of Atrial Fibrillation.” IEEE Transactions on Biomedical Engineering, vol. 48, No. 1, Jan. 2001CrossRefGoogle Scholar
  2. 2.
    M. Fukunami, T. Yamada, M. Ohmori, K. Kumagai, K. Umemoto, A. Sakai, N. Kondoh, T. Minamino, and N. Hoki, “Detection of Patients at Risk for Paroxysmal Atrial Fibrillation During Sinus Rhythm by P Wave-Triggered Signal-Averaged Electrocardiogram.” Aug 1990Google Scholar
  3. 3.
    Xiaolin Zhou, Hongxia Ding, Benjamin Ung, Emma Pickwell-MacPherson and Yuanting Zhang, “Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy.” BioMedical Engineering OnLine, 2014Google Scholar
  4. 4.
    Rodrigo V. Andreao, Bernadette Dorizzi, and Jerome Boudy, “ECG Signal Analysis Through Hidden Markov Models.” IEEE Transactions on Biomedical Engineering, vol. 53, No. 8, Aug. 2006Google Scholar
  5. 5.
    S. Dash, K.H. Chon, S. Lu, and E.A. Raeder, “Automatic Real Time Detection of Atrial Fibrillation.” Annals of Biomedical Engineering, Vol. 37, No. 9, Sep 2009, pp. 1701–1709CrossRefGoogle Scholar
  6. 6.
    Linda Argote, Paul Ingram, John M. Levine and Richard L. Moreland, “Knowledge Transfer in Organizations: Learning from the Experience of Others.” Organizational Behavior and Human Decision Processes, vol. 82, No. 1, May. 2000, pp. 1C8Google Scholar
  7. 7.
    Jun Yang, Rong Yan, and Alexander G. Hauptmann, “Cross domain video concept detection using adaptive svms.” 15th International Conference on Multimedia, pages 188–197, ACM, 2007Google Scholar
  8. 8.
    Goldberger A.L., Amaral L.A.N., Glass L., Hausdorff J.M., Ivanov P.Ch., Mark R.G., Mietus J.E., Moody G.B., Peng C.-K., Stanley H.E. PhysioBank, PhysioToolkit and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215–e220 [Circulation Electronic Pages;]; 2000 (June 13)
  9. 9.
    J. Huang, A. Smola, A. Gretton, K. M. Borgwardt, and B. Scholkopf, Correcting sample selection bias by unlabeled data. In Proceedings of the 19th Annual Conference on Neural Information Processing Systems, 2007Google Scholar
  10. 10.
    C. Cortes and V. Vapnik, “Support vector networks.” Machine Learning, Vol. 20, No. 3, pp. 273–279, 1995zbMATHGoogle Scholar
  11. 11.
    L.J.P. van der Maaten and G.E. Hinton, “Visualizing High-Dimensional Data Using t-SNE.” Nov. 2008, pp. 2579–2605Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina

Personalised recommendations