Motion Artifact Reduction in Electrocardiogram Using Adaptive Filtering Based on Skin-Potential Variation Monitoring

  • Shumei Dai
  • Dongyi ChenEmail author
  • Fan Xiong
  • Zhenghao Chen
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


Wearable devices which measure electrocardiogram (ECG) for continuous and real-time health monitoring become increasingly popular; ECG signals measured by textile electrodes in wearable devices can be easily disturbed by motion artifacts, which can cause misdiagnoses, leading to inappropriate treatment decisions. In this study, a simple method was demonstrated to measure skin-potential variation (SPV). SPV signals were obtained by two additional textile electrodes, which were positioned adjacent to the ECG sensing electrodes and connected with a resistance. Motion artifacts are adaptively filtered by using SPV as the reference variable. The results demonstrate that this device and method can significantly reduce skin-potential variation induced ECG artifacts.


ECG SPV Motion artifacts Textile electrodes 



This work is supported by National Natural Science Foundation of China (no. 61572110) and National Key Research & Development Plan of China (no. 2016YFB1001401).


  1. 1.
    Chi, Y.M., Jung, T.-P., Cauwenberghs, G.: Dry-contact and noncontact biopotential electrodes: methodological review. IEEE Rev. Biomed. Eng. 3, 106–119 (2010)CrossRefGoogle Scholar
  2. 2.
    Yao, S., Zhu, Y.: Nanomaterial-enabled dry electrodes for electrophysiological sensing: a review. JOM. 68(4), 1145–1155 (2016)CrossRefGoogle Scholar
  3. 3.
    Xu, P.J., Zhang, H., Tao, X.M.: Textile-structured electrodes for electrocardiogram. Text. Prog. 40(4), 183–213 (2008)CrossRefGoogle Scholar
  4. 4.
    Wiese, S.R., et al.: Electrocardiographic motion artifact versus electrode impedance. IEEE Trans. Biomed. Eng. 52(1), 136–139 (2005)CrossRefGoogle Scholar
  5. 5.
    Weihua, P., et al.: Skin-potential variation insensitive dry electrodes for ECG recording. IEEE Trans. Biomed. Eng. 64(2), 463–470 (2017)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Lu, G., et al.: Removing ECG noise from surface EMG signals using adaptive filtering. Neurosci. Lett. 462(1), 14–19 (2009)CrossRefGoogle Scholar
  7. 7.
    Pengjun, X., Xiaoming, T., Shanyuan, W.: Measurement of wearable electrode and skin mechanical interaction using displacement and pressure sensors. In: 2011 4th International IEEE Conference on Biomedical Engineering and Informatics (BMEI), vol. 2, 2011Google Scholar
  8. 8.
    Tong, D.A.: Electrode systems and methods for reducing motion artifact. U.S. Patent No. 6,912,414, 28 Jun 2005Google Scholar
  9. 9.
    Tong, D.A., Bartels, K.A., Honeyager, K.S.: Adaptive reduction of motion artifact in the electrocardiogram. In: Proceedings of the Second Joint. IEEE Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002, vol. 2, 2002Google Scholar
  10. 10.
    Liu, Y., Pecht, M.G.: Reduction of skin stretch induced motion artifacts in electrocardiogram monitoring using adaptive filtering. In: Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE, IEEE, 2006Google Scholar
  11. 11.
    Byung-hoon, K. et al.: Motion artifact reduction in electrocardiogram using adaptive filtering based on half cell potential monitoring. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2012Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shumei Dai
    • 1
  • Dongyi Chen
    • 1
    Email author
  • Fan Xiong
    • 1
  • Zhenghao Chen
    • 1
  1. 1.University of Electronic Science and Technology of China, School of Automation EngineeringChengduChina

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