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Proceedings Paper

ECG signals denoising using wavelet transform and independent component analysis
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Paper Abstract

A method of two channel exercise electrocardiograms (ECG) signals denoising based on wavelet transform and independent component analysis is proposed in this paper. First of all, two channel exercise ECG signals are acquired. We decompose these two channel ECG signals into eight layers and add up the useful wavelet coefficients separately, getting two channel ECG signals with no baseline drift and other interference components. However, it still contains electrode movement noise, power frequency interference and other interferences. Secondly, we use these two channel ECG signals processed and one channel signal constructed manually to make further process with independent component analysis, getting the separated ECG signal. We can see the residual noises are removed effectively. Finally, comparative experiment is made with two same channel exercise ECG signals processed directly with independent component analysis and the method this paper proposed, which shows the indexes of signal to noise ratio (SNR) increases 21.916 and the root mean square error (MSE) decreases 2.522, proving the method this paper proposed has high reliability.

Paper Details

Date Published: 5 August 2015
PDF: 8 pages
Proc. SPIE 9622, 2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 962213 (5 August 2015); doi: 10.1117/12.2193108
Show Author Affiliations
Manjin Liu, Beijing Institute of Technology (China)
Mei Hui, Beijing Institute of Technology (China)
Ming Liu, Beijing Institute of Technology (China)
Liquan Dong, Beijing Institute of Technology (China)
Zhu Zhao, Beijing Institute of Technology (China)
Yuejin Zhao, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 9622:
2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology
Guangming Shi; Xuelong Li; Bormin Huang, Editor(s)

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