Abstract
The electrocardiogram signal consists in a character of smaller amplitude together with a larger interference range and the reconstructed signal, according to the classical compressed sensing theory, cannot be accurately conveyed by the signal. To solve this problem, compressed sensing based on the wavelet transform was stressed on. We carry out a compressed sensing algorithm based on wavelet transform, thus is to use the wavelet decomposition to separate the electrocardiogram, to reduce the noise pollution, to compress and reconstruct the high-frequency coefficient and to recover the signal by inversing the wavelet transform. Meanwhile, analysis on the data effect was also made. The result of the simulation shows that it obviously proves the noise suppressing effect on combining wavelet transform with compressed sensing to recover the signal. The integrity of useful information is enhanced, as well as obtaining a higher signal-to-noise ratio.
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Sun, J. (2018). Electrocardiogram Signal De-noising and Reconstruction Based on Compressed Sensing. In: Liang, Q., Mu, J., Wang, W., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2016. Lecture Notes in Electrical Engineering, vol 423. Springer, Singapore. https://doi.org/10.1007/978-981-10-3229-5_66
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DOI: https://doi.org/10.1007/978-981-10-3229-5_66
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