Sparse Sampling and Reconstruction Algorithm of Electrocardiogram Signal in Compressed Sensing

被引：0
|
作者：
Qi L. [1 ]
Xing J.-Z. [1 ]
Chen J.-X. [1 ]
Zhang L.-Y. [1 ]
机构：
[1] Sino-Dutch Biomedical & Information Engineering School, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2019年 / 40卷 / 08期
关键词：
Acquisition and reconstruction; Compressed sensing; Electrocardiogram signal; Wavelet basis;
D O I：
10.12068/j.issn.1005-3026.2019.08.005
中图分类号：
学科分类号：
摘要：
Real-time electrocardiogram(ECG) monitoring will result in large data volume, which brings about severe pressure on the transmission and storage of the system. In order to reduce the computational complexity of data acquisition and the data volume in transmission, compressed sensing was used for the ECG signal. Taking time and error of signal reconstruction as critical indicators, the performance of different reconstruction algorithms and wavelet basis are comprehensively studied. The results demonstrate that when the compression ratio is within 30%, the percentage mean-square difference of the basis tracking algorithm is less than 4%, and the reconstruction takes the shortest time. When the compression ratio is within 70%, the error of subspace tracking algorithm is less than 10%, and the reconstruction time is always the lowest. However, the optimal wavelet basis is generally related to the specific compression ratio. © 2019, Editorial Department of Journal of Northeastern University. All right reserved.
引用
收藏
页码：1087 / 1092and1098
相关论文
共 21 条
  • [1] Chen W.-W., Gao R.-L., Liu L.-S., Et al., Chinese cardiovascular disease report 2017, Chinese Circulation Journal, 33, 1, pp. 1-8, (2018)
  • [2] Pooyan M., Taheri A., Moazami-Goudarzi M., Et al., Wavelet compression of ECG signals using SPIHT algorithm, International Journal of Signal Processing, 1, 3, pp. 219-225, (2004)
  • [3] Qu X.-C., Zhang Y., Real-time ECG compression algorithm based on compressed sensing, Computer Engineering and Design, 35, 10, (2014)
  • [4] Mishra A., Thakkar F., Modi C., Et al., Comparative analysis of wavelet basis functions for ECG signal compression through compressive sensing, International Journal of Computer Science and Telecommunications, 3, 4, pp. 23-31, (2012)
  • [5] Candes E.J., Romberg J., Tao T., Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, 52, 2, pp. 489-509, (2006)
  • [6] Donoho D.L., Compressed sensing, IEEE Transactions on Information Theory, 52, 4, pp. 1289-1306, (2006)
  • [7] Zheng Y.-X., Jiang H.-M., Jin B., Et al., An image denoising scheme based on adaptive compressive sensing, Communications Technology, 46, 3, (2013)
  • [8] Candes E.J., Compressive sampling, Proceedings of the International Congress of Mathematicians, pp. 1433-1452, (2006)
  • [9] Wu H., Lu Z.-X., Speech recognition based on combined enhancement of Symlets wavelet and subspace, Computer Engineering and Applications, 47, 5, pp. 141-145, (2011)
  • [10] Candes E.J., The restricted isometry property and its implications for compressed sensing, Comptes Rendus Mathematique, 346, 9-10, pp. 589-592, (2008)
123