Advertisement

Investigation on Daubechies Wavelet-Based Compressed Sensing Matrices for ECG Compression

  • Yuvraj V. Parkale
  • S. L. Nalbalwar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)

Abstract

In this paper, we have investigated the different Daubechies (DB) wavelet-based compressed sensing (CS) matrices, namely db3, db4, db5, db6, db7, db8, db9, and db10 measurement matrices for ECG compression. The performance of the proposed Daubechies wavelet-based measurement matrices and state-of-the-art measurement matrices are evaluated using different performance measures such as Compression Ratio (CR), PRD, SNR, RMSE, and signal reconstruction time. The result demonstrates that the db3 and db10 measurement matrices outperform the state-of-the-art measurement matrices. Moreover, db3 and db4 measurement matrices show superior performance compared to db4, db5, db6, db7, db8, and db9 measurement matrices. Thus, this study exhibits the successful implementation of Daubechies (DB) wavelet-based sensing matrices for ECG compression.

Keywords

Wavelet transform Compressed sensing (CS) ECG compression 

References

  1. 1.
    Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Baraniuk, R.G.: Compressive sensing. IEEE Sig. Process. Mag. 118–121 (2007).  https://doi.org/10.1109/msp.2007.4286571CrossRefGoogle Scholar
  3. 3.
    Candes, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Sig. Process. Mag. 21–30 (2008).  https://doi.org/10.1109/msp.2007.914731CrossRefGoogle Scholar
  4. 4.
    Lustig, M., Donoho, D.L., Santos, J.M., Pauly, J.M.: Compressed sensing MRI. IEEE Sig. Process. Mag. 25(8), 72–82 (2008).  https://doi.org/10.1109/MSP.2007.914728CrossRefGoogle Scholar
  5. 5.
    Guan, X., Yulong, G., Chang, J., Zhang, Z.: Advances in theory of compressive sensing and applications in communication. In: Proceedings of IEEE First International Conference on Instrumentation, Measurement, Computer, Communication and Control, pp. 662–665 (2011).  https://doi.org/10.1109/imccc.2011.169
  6. 6.
    Zhang, Z., Jung, T.-P., Makeig, S., Rao, B.D.: Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning. IEEE Trans. Biomed. Eng. 60(2), 300–309 (2013)CrossRefGoogle Scholar
  7. 7.
    Parkale, Y.V., Nalbalwar, S.L.: Application of compressed sensing (CS) for ECG signal compression: a review. Springer-Advances in Intelligent Systems and Computing (Springer-AISC), vol. 469, pp. 53–65.  https://doi.org/10.1007/978-981-10-1678-3_5Google Scholar
  8. 8.
    Polania, L.F., Carrillo, R.E., Blanco-Velasco, M., Barner, K.E.: Compressed sensing based method for ECG compression. In: Proceedings of ICASSP, pp. 761–764 (2011)Google Scholar
  9. 9.
    Polania, L.F., Carrillo, R.E., Blanco-Velasco, M., Barner, K.E.: Exploiting prior knowledge in compressed sensing wireless ECG systems. IEEE J. Biomed. Health Inform. (2014)Google Scholar
  10. 10.
    Chae, D.H., Alem, Y.F., Durrani, S., Kennedy, R.A.: Performance study of compressive sampling for ECG signal compression in noisy and varying sparsity acquisition. In: Proceedings of ICASSP, pp. 1306–1309 (2013)Google Scholar
  11. 11.
    Ansari-Ram, F., Hosseini-Khayat, S.: ECG signal compression using compressed sensing with nonuniform binary matrices. In: 16th CSI International Symposium on, Artificial Intelligence and Signal Processing (AISP), pp. 305–309 (2012)Google Scholar
  12. 12.
    Mishra, A., Thakkar, F., Modi, C., Kher, R.: ECG signal compression using compressive sensing and wavelet transform. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3404–3407 (2012)Google Scholar
  13. 13.
    Mishra, A., Thakkar, F.N., Modi, C., Kher, R.: Selecting the most favorable wavelet for compressing ECG signals using compressive sensing approach. International Conference on Communication Systems and Network Technologies (CSNT), pp. 128–132 (2012)Google Scholar
  14. 14.
    Mishra, A., Thakkar, F., Modi, C., Kher, R.: Comparative analysis of wavelet basis functions for ECG signal compression through compressive sensing. Int. J. Comput. Sci. Telecommun. 3, 9 (2012)Google Scholar
  15. 15.
    Parkale, Y.V., Nalbalwar, S.L.: Application of 1-D discrete wavelet transform based compressed sensing matrices for speech compression. J. SpringerPlus 5(1), 1–60 (2016).  https://doi.org/10.1186/s40064-016-3740-xCrossRefGoogle Scholar
  16. 16.
    Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM Rev. 43(1), 129–159 (2001)MathSciNetCrossRefGoogle Scholar
  17. 17.
    MIT-BIH Arrhythmia Database. www.physionet.org.  https://doi.org/10.13026/c2f305

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of E & TC EngineeringDr. Babasaheb Ambedkar Technological University (DBATU)Lonere, RaigadIndia

Personalised recommendations