Design and Implementation of Hybrid Compression Algorithm for Personal Health Care Big Data Applications | Wireless Personal Communications
Skip to main content

Advertisement

Springer Nature Link
Log in

Design and Implementation of Hybrid Compression Algorithm for Personal Health Care Big Data Applications

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Advanced telemedicine requires the gathering of big data through wireless body area network or internet of things based applications. These networks perform several tasks that consume maximum energy while transmitting the big data, which in turn discharges the battery and would require a battery replacement frequently. Also, the big data to be transferred and stored would require a significant amount of storage space. The above problem is rectified by compressing the big data acquired from the sensors before transmission that would reduce the consumption of power as well as use the storage space efficiently. In this paper, a hybrid compression algorithm (HCA) based on Rice Golomb Coding is proposed. The efficiency of the proposed compression algorithm is tested on ECG data from the physionet ATM database and real-time data acquired from the ECG sensor. The proposed HCA comprises of both lossy and lossless compression. The real-time implementation of the proposed compression algorithm is carried out using NI myRIO hardware and LabVIEW graphical tool. The compressed data then stored in the Google cloud, and the analysis of storage space using the HCA shows a reduction of 70% storage space for 10 minutes of ECG data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Elgendi, M. (2018). Less is more in biosignal analysis: Compressed data could open the door to faster and better diagnosis. Diseases. https://doi.org/10.3390/diseases6010018.

    Article  Google Scholar 

  2. Tornekar, R. V., & Gajre, S. S. (2017). Comparative study of lossless ECG signal compression techniques for wireless networks. Computing in Cardiology. https://doi.org/10.22489/cinc.2017.095-236.

    Article  Google Scholar 

  3. Mitra, S., & Das, D. (2017). A critical study on the applications of run-length encoding techniques in combined encoding schemes. International Journal of Advanced Research in Computer Science,8(5), 2556–2561.

    Google Scholar 

  4. Elgendi, M., Mohamed, A., & Ward, R. (2017). Efficient ECG compression and QRS detection for e-health applications. Scientific Reports. https://doi.org/10.1038/s41598-017-00540-x.

    Article  Google Scholar 

  5. Kalaivani, S., & Tharini, C. (2018). Analysis and modification of rice golomb coding lossless compression algorithm for wireless sensor networks. Journal of Theoretical And Applied Information Technology,96(12), 3802–3814.

    Google Scholar 

  6. Sun, C.-C., & Tai, S.-C. (2005). Beat-based ECG compression using gain-shape vector quantization. IEEE Transaction on Biomedical Engineering,52, 1882–1888.

    Article  Google Scholar 

  7. Rasool, U., Mairaj, S., Nazeer, T., & Ahmed, S. (2017). Wavelet-based image compression techniques: comparative analysis and performance evaluation. International Journal of Emerging Technologies in Engineering Research (IJETER),5(9), 9–13.

    Google Scholar 

  8. Chouakri, S. A., Benaiad, M. M., & Taleb Ahmed, A. (2011). Run-length encoding and Wavelet transform-based ECG compression algorithm for transmission via IEEE802.11b WLAN channel. In ACM digital library, proceedings of the 4th international symposium on applied sciences in biomedical and communication technologies, Article No. 37, Barcelona, Spain, October 26–29, 2011.

  9. Javaid, R., Besar, R., & Abas, F. S. (2017). Performance evaluation of percent root mean square difference for ECG signals compression. An International Journal on Signal Processing,48, 1–9.

    Google Scholar 

  10. Tan, C., Zhang, L., & Wu, H.-T. (2019). A novel blaschke unwinding adaptive-fourier-decomposition-based signal compression algorithm with application on ECG signals. IEEE Journal of Biomedical and Health Informatics,23(2), 672–682.

    Article  Google Scholar 

  11. Burguera, A. (2019). Fast QRS detection and ECG compression based on signal structural analysis. IEEE Journal of Biomedical and Health Informatics,23(1), 123–131.

    Article  Google Scholar 

  12. Tsai, T.-H., & Kuo, W.-T. (2018) An efficient ECG lossless compression system for embedded platforms with telemedicine applications. In IEEE.

  13. Huang, H., Hu, S., & Sun, Y. (2018). ECG signal compression for low-power sensor nodes using sparse frequency spectrum features. In IEEE biomedical circuits and systems conference (BioCAS).

  14. Deepu, C. J., Heng, C.-H., & Lian, Y. (2017). A hybrid data compression scheme for power reduction in wireless sensors for IoT. IEEE Transactions on Biomedical Circuits and Systems,11(2), 245–254.

    Article  Google Scholar 

  15. Jha, C. K., & Kolekar, M. H. (2017). ECG data compression algorithm for telemonitoring of cardiac patients. International Journal of Telemedicine and Clinical Practices,2(1), 31–41.

    Article  Google Scholar 

  16. Lu, X., Pan, M., Yu, Y. (2018). QRS detection based on improved adaptive threshold. Journal of Healthcare Engineering,2018, Article ID 5694595.

  17. Kalaivani, S., Shahnaz, I., Shirin, S. R., & Tharini, C. (2016). Real-time ECG acquisition and detection of anomalies. In Dash, S. S., Bhaskar, M. A., Panigrahi, B. K., Das, S (Eds.), Artificial intelligence and evolutionary computations in engineering systems. Springer.

  18. Uthayakumar, J., Venkattaraman, T., & Dhayachelvan, P. (2018). A survey on data compression techniques: From the perspective of data quality, coding schemes, data types and applications. Journal of King Saud University- Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2018.05.006.

    Article  Google Scholar 

  19. https://learn.ni.com.

  20. https://physionet.org/cgibin/atm/ATM?database=mitdb&tool=plot_waveforms.

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, B.S.A Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India

    S. Kalaivani, C. Tharini, K. Saranya & Kosireddy Priyanka

Authors
  1. S. Kalaivani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Tharini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Saranya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kosireddy Priyanka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Kalaivani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kalaivani, S., Tharini, C., Saranya, K. et al. Design and Implementation of Hybrid Compression Algorithm for Personal Health Care Big Data Applications. Wireless Pers Commun 113, 599–615 (2020). https://doi.org/10.1007/s11277-020-07241-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07241-1

Keywords

Search

Navigation