This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Wearable Electrocardiogram (ECG) is attracting much attention in daily healthcare applications. From the viewpoint of long-term use, it is desired that the electrodes are non-contact with the human body. In this study, the authors propose an algorithm using the stationary wavelet transform (SWT) to remove motion artefact superimposed on ECG signal when using non-contact capacitively coupling electrodes. The authors evaluate the effect on motion artefact removal of this algorithm by applying it to various ECG signals with motion artefacts superimposed. As a result, the correlation coefficients of ECG signals with respect to the clean ones have been improved from 0.71 to 0.88 on median before and after motion artefact removal, which demonstrates the validity of the proposed SWT-based algorithm.
References
-
-
1)
-
2. Wang, J., Wang, Q.: ‘Body area communications’ (Wiley-IEEE, 2012).
-
2)
-
3. Wang, J., Fujiwara, T., Kato, T., et al: ‘Wearable ECG based on impulse radio type human body communication’, IEEE Trans. Biomed. Eng., 2015, 63, (9), pp. 1887–1894 (doi: 10.1109/TBME.2015.2504998).
-
3)
-
4. Sakuma, J., Anzai, D., Wang, J.: ‘Performance of human body communication-based wearable ECG with capacitive coupling electrodes’, Healthcare Technol. Lett., 2016, 3, (3), pp. 222–225 (doi: 10.1049/htl.2016.0023).
-
4)
-
5. Kishimoto, Y., Kutsuna, Y., Oguri, K.: ‘Detecting motion artifact ECG noise during sleeping by means of a tri-axis accelerometer’. Proc. IEEE EMBS, 2007.
-
5)
-
25. Goldberger, A.L., Amaral, L.A.N., Glass, L., et al: ‘PhysioBank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals’, Circulation, 2000, 101, (23), pp. e215–e220 (doi: 10.1161/01.CIR.101.23.e215).
-
6)
-
1. IEEE Std 802.15.6-2012: ‘IEEE Standard for local and metropolitan area networks – Part 15.6: Wireless Body Area Networks’, 2012.
-
7)
-
24. Moody, G.B., Mark, R.G.: ‘The impact of the MIT-BIH Arrhythmia Database’, IEEE Eng. Med. Biol., 2001, 20, (3), pp. 45–50 (doi: 10.1109/51.932724).
-
8)
-
6. Strasser, F., Muma, M., Zoubir, A.M.: ‘Motion artifact removal in ECG signals using multi-resolution thresholding’. Proc. European Signal Processing Conf. (EUSIPCO), 2012, pp. 899–903.
-
9)
-
26. Mallat, S.: ‘A theory for multiresolution signal decomposition: The wavelet representation’, IEEE Trans. Pattern Anal. Mach. Intell., 1989, 11, (7), pp. 674–693 (doi: 10.1109/34.192463).
-
10)
-
8. Nason, G.P., Silverman, B.W.: ‘The stationary wavelet transform and some statistical applications’, Lect. Notes Stat., 1995, 103, pp. 281–299 (doi: 10.1007/978-1-4612-2544-7_17).
-
11)
-
9. Lugovaya, T.S.: ‘Biometric human identification based on electrocardiogram’. , Faculty of Computing Technologies and Informatics, Electrotechnical University ‘LETI’, Saint-Petersburg, Russian Federation, 2005.
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