Patient Confidential Information Transmission Using the Integration of PSO-Based Biomedical Signal Steganography and Threshold-Based Compression

Abstract

Purpose

Electrocardiogram (ECG) signals aid in the clinical assessment of essential body functions by measuring pulse rate, intracranial pressure, respiration rate, and blood pressure. Additionally, ECG signals are employed to identify various heart diseases, such as arrhythmias and myocardial damage. This study aims to reduce the data size of ECG signals while preserving their original ECG characteristics and protecting personal privacy during network transmission.

Methods

First, we performed amplitude-quantization steganography on ECG signals to hide confidential patient data. We adopted a threshold-based compression technique to reduce the data size of ECG signals while preserving their characteristics as much as possible. We utilized a cubic spline in the recovery of the compressed ECG signal. In addition, the performance of the proposed amplitude-quantization steganography was enhanced by the particle swarm optimization algorithm.

Results

Experimental results verified the efficiency of the proposed method.

Conclusion

The proposed method not only protect the security of the ECG transmission but also reduce the amount of ECG transmission. Moreover, the proposed method improves the drawback that the quality of each hidden ECG signal is greatly reduced as the quantization size Q is increased.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Data Availability

All test ECG data are open source from MIT-BIH Arrhythmia Database Directory, Harvard University-Massachusetts Institute of Technology Division of Health Science and Technology, July, 1992. http://www.ph.

References

  1. 1.

    Xialong He, K.-K. Tseng, H.-N. Huang, S.-T. Chen, S.-Y. Tu, F. Zeng, and J.-S. Pan, “Wavelet-based Quantization Watermarking for ECG Signals,”IEEE International Conference on Computing Measurement Control and Sensor Network (CMCSN-2012), pp.233–236, 2012.

  2. 2.

    Dey, N., Mukhopadhyay, S., Das, A., & Chaudhuri, S. S. (2012). Analysis of P-QRS-T components modified by blind watermarking technique within the electrocardiogram signal for authentication in wireless telecardiology using DWT. International Journal of Image, Graphics, and Signal Process, 4(7), 33–46.

    Article  Google Scholar 

  3. 3.

    Ayman, I., & Ibrahim, K. (2013). Wavelet-Based ECG steganography for protecting patient confidential information in point-of-care systems. IEEE Transactions on Biomedical Engineering, 60, 3322–3330.

    Article  Google Scholar 

  4. 4.

    Chen, S.-T., Guo, Y.-J., Huang, H.-N., Kung, W.-M., Tseng, K.-K., & Tu, S.-Y. (2014). Hiding patients confidential data in the ECG signal via transform-domain quantization scheme. Journal of Medical Systems. https://doi.org/10.1007/s10916-014-0054-9

    Article  PubMed  Google Scholar 

  5. 5.

    Fira, C. M., & Goras, L. (2008). An ECG signals compression method and its validation using NNs. IEEE Transactions on Biomedical Engineering, 55(4), 1319–1326.

    Article  Google Scholar 

  6. 6.

    Lee, S., Kim, J., & Lee, M. (2011). A real-time ECG data compression and transmission algorithm for an E-health device. IEEE Transactions on Biomedical Engineering, 58(9), 2448–2455.

    Article  Google Scholar 

  7. 7.

    Mamaghanian, H., Khaled, N., Atienza, D., & Vandergheynst, P. (2011). Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. IEEE Transactions on Biomedical Engineering, 58(9), 2456–2466.

    Article  Google Scholar 

  8. 8.

    Abo-Zahhad, M., Ahmed, S. M., & Zakaria, A. (2012). An efficient technique for compressing ECG signals using QRS detection, estimation, and 2D DWT coefficients thresholding. Modelling and Simulation in Engineering. https://doi.org/10.1155/2012/742786

    Article  Google Scholar 

  9. 9.

    Agulhari, C. M., Bonatti, I. S., & Peres, P. L. D. (2013). An adaptive run length encoding method for the compression of electrocardio grams. Medical Engineering & Physics, 35(2), 145–153.

    Article  Google Scholar 

  10. 10.

    Ma, J. L., Zhang, T. T., & Dong, M. C. (2015). A novel ECG Data compression method using adaptive fourier decomposition with security guarantee in E-health applications. IEEE Journal of Biomedical and Health Informatics, 19(3), 986–994.

    Article  Google Scholar 

  11. 11.

    Adamo, A., Grossi, G., Lanzarotti, R., & Lin, J. (2015). ECG compression retaining the best natural basis K-coefficients via sparse decomposition. Biomedical Signal Processing and Control, 15, 11–17.

    Article  Google Scholar 

  12. 12.

    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  PubMed  PubMed Central  Google Scholar 

  13. 13.

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

    Article  Google Scholar 

  14. 14.

    Elgendi, M., Al-Ali, A., Mohamed, A., & Ward, R. (2018). Improving remote health monitoring: A low-complexity ECG compression approach. Diagnostics (Basel), 8(1), 1–17.

    Google Scholar 

  15. 15.

    Hu, Y.-H., Li, Y.-H., & Zhao, M. (2019). Integration of Information Hiding and Compression for Biomedical Signals. Journal of Internet Technology, 20(3), 975–982.

    Google Scholar 

  16. 16.

    Chen, S.-T., Huang, H.-N., Chen, C.-J., & Wu, G.-D. (2010). Energy-proportion based scheme for audio watermarking. IET Proceedings on Signal Processing, 4(5), 576–587.

    Article  Google Scholar 

  17. 17.

    Chen, S.-T., Wu, G.-D., & Huang, H.-N. (2010). Wavelet-domain audio watermarking scheme using optimization-based quantization. IET Proceedings on Signal Processing, 4(6), 720–727.

    Article  Google Scholar 

  18. 18.

    Chen, S.-T., Hsu, C.-Y., & Huang, H.-N. (2015). Wavelet-domain audio watermarking using optimal modification on low-frequency amplitude. IET Proceedings on Signal Processing, 9(2), 166–176.

    Article  Google Scholar 

  19. 19.

    Chen, S.-T., Huang, H.-N., Chen, C.-C., Tseng, K.-K., & Tu, S.-Y. (2013). Adaptive audio watermarking via the optimization point of view on wavelet-based entropy. Digital Signal Processing, 23(3), 971–980.

    Article  Google Scholar 

  20. 20.

    Huang, H.-N., Chen, S.-T., Lin, M.-S., Kung, W.-M., & Hsu, C.-Y. (2015). Optimization-based embedding for wavelet-domain audio watermarking. Journal of Signal Processing Systems, 80(2), 197–208.

    Article  Google Scholar 

  21. 21.

    Zhan, Z.-H., Zhang, J., Li, Y., & Chung, H.S.-H. (2009). Adaptive particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics, 39(6), 1362–1381.

    Article  Google Scholar 

  22. 22.

    Zhan, S.-M., Chen, Z.-H., Gong, W.-N., Zhang, Y.-J., & Yun, J.-L. (2014). Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks. IEEE Transactions on Industrial Electronics, 61(12), 1362–1381.

    Google Scholar 

  23. 23.

    MIT-BIH Arrhythmia Database Directory, Harvard University-Massachusetts Institute of Technology Division of Health Science and Technology, July, 1992. http://www.ph

Download references

Acknowledgements

No acknowledgements.

Funding

No funding.

Author information

Affiliations

Authors

Contributions

Conceptualization, S-TC and S-JL; methodology, S-TC and S-JL; software, S-TC; validation, S-YC, M-CT, M-DT, and Y-JT; writing—review and editing, S-TC, L–H, W and S-JL; All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Shuo-Tsung Chen.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 212 KB)

Appendix

Appendix

See Figs. 9 , 10 , 11

Fig. 9
figure9

The results of searching position and fitness value in PSO for dataset ID 100 with M  = 2 and Q  = 100

Fig. 10
figure10

The results of searching position and fitness value in PSO for dataset ID 100 with M  = 2 and Q  = 1000

Fig. 11
figure11

The results of searching position and fitness value in PSO for dataset ID 100 with M  = 2 and Q  = 4096

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chen, SY., Lin, SJ., Tsai, MC. et al. Patient Confidential Information Transmission Using the Integration of PSO-Based Biomedical Signal Steganography and Threshold-Based Compression. J. Med. Biol. Eng. (2021). https://doi.org/10.1007/s40846-021-00641-z

Download citation

Keywords

  • ECG signals
  • Network transmission
  • Patent confidential data
  • Particle Swarm Optimization