Skip to main content
SpringerLink
Log in

Watermarking of ECG signals compressed using Fourier decomposition method

  • 1205: Emerging Technologies for Information Hiding and Forensics in Multimedia Systems
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Secure and efficient transmission of biomedical signals containing delicate and vital health information of a subject over the network is challenging. Digital watermarking is an attractive choice for the secure and reliable transmission of biomedical signals using smart healthcare devices. This paper proposes a method that integrates watermarking and compression of an electrocardiogram (ECG) of a subject. ECG watermarking secures the cardiac information of a subject, while signal compression reduces the amount of data. In this study, a sequence of binary ones and zeros are used as a watermark to secure the ECG signal. The watermarked ECG signal is analyzed and compressed by the Fourier decomposition method by removing the redundant component present in the ECG signal. The proposed method is validated using the MIT-BIH arrhythmia database. Percentage root-mean-square difference (PRD), the output signal to noise ratio (SNR), compression ratio, and computation time are used to evaluate the performance of the proposed method. Results show that the proposed method reduces the PRD by a factor of three, and the compression ratio increases by a factor of two. The proposed algorithm is evaluated at different input SNR values. The improved output SNR demonstrates the denoising capabilities of the watermarked signal.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Act A (1996) Health insurance portability and accountability act of 1996. Public law 104:191

    Google Scholar 

  2. Ajdaraga E, Gusev M (2017) Analysis of sampling frequency and resolution in ECG signals. In: 2017 25th Telecommunication Forum (TELFOR), p. 1–4. IEEE, 2017

  3. Al-Nashash HAM (1995) A dynamic Fourier series for the compression of ECG using FFT and adaptive coefficient estimation. Med Eng Phys 17(3):197–203

    Article  Google Scholar 

  4. American Heart Association, AHA Database, ECRI, Butler Pike, Plymouth Meeting, PA, USA

  5. Averkiou M (2015) Digital watermarking. Department of Computer Science University of Cyprus

  6. Batista LV, Melcher EUK, Carvalho LC (2001) Compression of ECG signals by optimized quantization of discrete cosine transform coefficients. Med Eng Phys 23(2):127–134

    Article  Google Scholar 

  7. Bousseljot R, Kreiseler D, Schnabel A (1995) Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet. Biomed Tech 1995:317–318

    Google Scholar 

  8. Chin W-L, Chang C-C, Tseng C-L, Huang Y-Z, Jiang T (2019) Bayesian real-time QRS complex detector for healthcare system. IEEE Internet Things J 6(3):5540–5549

    Article  Google Scholar 

  9. Cox I, Miller M, Bloom J, Fridrich J, Kalker T (2007) Digital watermarking and steganography. Morgan Kaufmann

  10. El B’charri O, Latif R, Jenkal W, Abenaou A (2016) The ECG signal compression using an efficient algorithm based on the DWT. Int J Adv Comput Sci Appl 1(7):181–187

    Google Scholar 

  11. Fathi A, Faraji-Kheirabadi F (2016) ECG compression method based on adaptive quantization of main wavelet packet subbands. SIViP 10(8):1433–1440

    Article  Google Scholar 

  12. Fatimah B, Singh P, Singhal A, Pachori RB (2020) Detection of apnea events from ECG segments using Fourier decomposition method. Biomed Signal Process Control 61:102005

    Article  Google Scholar 

  13. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet – components of a new research resource for complex physiologic signals. Circulation 101:E215–E220

    Google Scholar 

  14. Gutiérrez-Rivas R, García JJ, Marnane WP, Hernández A (2015) Novel real-time low-complexity QRS complex detector based on adaptive thresholding. IEEE Sens J 15(10):6036–6043

    Article  Google Scholar 

  15. Hammad M, Maher A, Wang K, Jiang F, Amrani M (2018) Detection of abnormal heart conditions based on characteristics of ECG signals. Measurement 125:634–644

    Article  Google Scholar 

  16. Hao H, Wang H, Rehman N, Chen L (2017) An improved multivariate wavelet denoising method using subspace projection. IEICE Trans Fund Electron Commun Comput Sci 100(3):769–775

    Article  Google Scholar 

  17. Iyengar N, Peng CK, Morin R, Goldberger AL, Lipsitz LA (1996) Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am J Physiology 271(4):R1078–R1084

    Google Scholar 

  18. Jalaleddine SMS, Hutchens CG, Strattan RD, Coberly WA (1990) ECG data compression techniques-a unified approach. IEEE Trans Biomed Eng 37(4):329–343

    Article  Google Scholar 

  19. Jero SE, Ramu P, Ramakrishnan S (2014) Discrete wavelet transform and singular value decomposition-based ECG steganography for secured patient information transmission. J R Med Sys 38(10):1–11

    Google Scholar 

  20. Jero SE, Ramu P, Ramakrishnan S (2015) ECG steganography using curvelet transform. Biomed Signal Process Control 22:161–169

    Article  Google Scholar 

  21. Kaur, S, R Singhal, O Farooq, and BS Ahuja (2010) Digital watermarking of ECG data for secure wireless communication. In: 2010 international conference on recent trends in information, telecommunication and computing, pp. 140–144. IEEE, 2010

  22. Kirovski D, Malvar HS (2003) Spread-spectrum watermarking of audio signals. IEEE Trans Signal Process 51(4):1020–1033

    Article  MathSciNet  Google Scholar 

  23. Kumar A, Komaragiri R, Kumar M (2018) From pacemaker to wearable: techniques for ECG detection systems. J Med Syst 42(2):1–17

    Article  Google Scholar 

  24. Kumar A, Komaragiri R, Kumar M (2019) Time-frequency localization using three-tap biorthogonal wavelet filter bank for electrocardiogram compressions. Biomed Eng Lett 9(3):407–411

    Article  Google Scholar 

  25. Kumar A, Komaragiri R, Kumar M (2019) Design of efficient fractional operator for ECG signal detection in implantable cardiac pacemaker systems. Int J Circuit Theory Appl 47(9):1459–1476

    Article  Google Scholar 

  26. Kumar A, Tomar H, Mehla VK, Komaragiri R, Kumar M (2020) Stationary wavelet transform based ECG signal denoising method. ISA Ttrans 114:251

    Article  Google Scholar 

  27. Kumar A, Ranganatham R, Singh S, Komaragiri R, Kumar M (2021) A robust digital ECG signal watermarking and compression using biorthogonal wavelet transform. Res Biomed Eng 37(1):79–85

    Article  Google Scholar 

  28. Laguna P, Mark RG, Goldberg A, Moody GB (1997) A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. In: Computers in cardiology, pp 673–676. IEEE, 1997

  29. Lee W, Kim S, Kim D (2018) Individual biometric identification using multi-cycle electrocardiographic waveform patterns. Sensors 18(4):1005

    Article  MathSciNet  Google Scholar 

  30. Malik M (1996) Task force of the European society of cardiology and the North American society of pacing and electrophysiology. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17:354–381

    Article  Google Scholar 

  31. Manikandan MS, Dandapat S (2014) Wavelet-based electrocardiogram signal compression methods and their performances: a prospective review. Biomed Signal Process Control 14:73–107

    Article  Google Scholar 

  32. Marcel S, del José R, Millán. (2007) Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Trans Pattern Anal Machine Intell 29(4):743–752

    Article  Google Scholar 

  33. Moody GB, Mark RG (2001) The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 20(3):45–50

    Article  Google Scholar 

  34. Pandey A, Singh B, Saini BS, Sood N (2016) A joint application of optimal threshold based discrete cosine transform and ASCII encoding for ECG data compression with its inherent encryption. Australas Phys Eng Sci Med 39(4):833–855

    Article  Google Scholar 

  35. Olmos S, MillAn M, Garcia J, Laguna P (1996) ECG data compression with the Karhunen-Loeve transform. In: Computers in cardiology 1996, pp. 253–256. IEEE, 1996

  36. Rhee MY (2003) Internet security: cryptographic principles, algorithms, and protocols. Wiley, New York

    Google Scholar 

  37. Sanivarapu PV, Rajesh KNVPS, Rajasekhar Reddy NV, Sekhar Reddy N (2020) Patient data hiding into ECG signal using watermarking in transform domain. Phys Eng Sci Med 43(1):213–226

    Article  Google Scholar 

  38. Sellami A, Zouaghi A, Daamouche A (2017) ECG as a biometric for individual's identification. In: 2017 5th International Conference on Electrical Engineering-Boumerdes (ICEE-B), pp. 1–6. IEEE, 2017

  39. Singh P, Joshi SD, Patney RK, Saha K (2017) The Fourier decomposition method for nonlinear and nonstationary time series analysis. Proc R Soc A 473(2199):20160871

    Article  Google Scholar 

  40. Singhal A, Singh P, Fatimah B, Pachori RB (2020) An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique. Biomed Signal Process Control 57:101741

    Article  Google Scholar 

  41. Taddei A, Distante G, Emdin M, Pisani P, Moody GB, Zeelenberg C, Marchesi C (1992) The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. Eur Heart J 13(9):1164–1172

    Article  Google Scholar 

  42. Tseng K-K, He X, Kung W-M, Chen S-T, Liao M, Huang H-N (2014) Wavelet-based watermarking and compression for ECG signals with verification evaluation. Sensors 14(2):3721–3736

    Article  Google Scholar 

  43. Zhang B, Zhao J, Chen X, Wu J (2017) ECG data compression using a neural network model based on multi-objective optimization. PLoS ONE 12(10):e0182500

    Article  Google Scholar 

  44. Zou D, Shi YQ, Ni Z, Su W (2006) A semi-fragile lossless digital watermarking scheme based on integer wavelet transform. IEEE Trans Circuits Syst Video Technol 16(10):1294–1300

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India

    Prashant Mani Tripathi & Rama Komaragiri

  2. School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

    Ashish Kumar

  3. Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India

    Manjeet Kumar

Authors
  1. Prashant Mani Tripathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rama Komaragiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manjeet Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manjeet Kumar.

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

Tripathi, P.M., Kumar, A., Komaragiri, R. et al. Watermarking of ECG signals compressed using Fourier decomposition method. Multimed Tools Appl 81, 19543–19557 (2022). https://doi.org/10.1007/s11042-021-11492-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11492-w

Keywords

Search

Navigation