A morphologically robust chaotic map based approach to embed patient’s confidential data securely in non-QRS regions of ECG signal

  • Neetika SoniEmail author
  • Indu Saini
  • Butta Singh
Scientific Paper


In e-healthcare paradigm, the physiological signals along with patient’s personal information need to be transmitted to remote healthcare centres. Before sharing this sensitive information over the unsecured channel, it is prerequisite to protect it from unauthorised access. The proposed method explores ECG signal as the cover signal to hide patient’s personal information without disturbing its diagnostic features. Chaotic maps are used to randomly select the embedding locations in the non-QRS region while excluding the sensitive QRS region of ECG train. Optimum Location Selection algorithm has been designed to select the embedding locations in non-QRS embedding region. The proposed algorithm is thoroughly examined and the distortion is measured in terms of statistical parameters and clinical measures such as PRD, PRDN, PRD1024, PSNR, SNR, MSE, MAE, KL-Divergence, WWPRD and WEDD. The robustness of the algorithm is verified using the parameters such as key space and key sensitivity. The implementation has been extensively tested on all the 48 records of the standard MIT-BIH Arrhythmia database, abnormal databases [CU-VT, BIDMC-CHF and PTB (leads I, II and III)] and self-recorded data of 20 subjects. The algorithm yields average PRD, MSE, KL-Divergence, PSNR, WWPRD and WEDD of 4.7 × 10−3, 1.13 × 10−5, 1.29 × 10−5, 50.28, 0.15 and 0.04 at an average maximum EC of 0.45(96876 bits) on MIT-BIH Arrhythmia database and 0.016, 3.38 × 10−5, 1.8 × 10−4, 46.03, 0.13 and 0.03 respectively at an average maximum EC of 0.47 (102571 bits) on self-recorded data which clearly reveals the competency of the proposed algorithm in comparison with the other state of the art ECG steganography approaches.


ECG steganography Chaotic maps Embedding capacity (EC) Key sensitivity Wavelet based weighted percentage root mean square difference (WWPRD) Wavelet energy based diagnostic distortion (WEDD) 



This research work does not receive any grants from any funding agency.

Compliance with ethical standards

Conflict of interest

No conflict of interest.

Ethical approval

The ethical principles for medical research of World Medical Association (WMA’s) Declaration of Helsinki have been followed for data acquisition.


  1. 1.
    Goceri E, Songul C (2018) Biomedical information technology: Image based computer aided diagnosis systems. In International conference on advanced technologies, Antalya, pp 132Google Scholar
  2. 2.
    Goceri E, Songul C (2018) Mobile health technologies for patients with mental illness. In International conference on advanced technologies, Antalya, pp. 146Google Scholar
  3. 3.
    English A, Summers R, Lewis J, Coleman C (2015) Confidentiality, third-party billing & the health insurance claims process: implications for title X. Accessed 11 Nov 2015Google Scholar
  4. 4.
    Subhedar MS, Mankar VH (2014) Current status and key issues in image steganography: a survey. Comput Sci Rev 13–14:95–113CrossRefGoogle Scholar
  5. 5.
    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 39:833–855CrossRefGoogle Scholar
  6. 6.
    Johnson NF, Jajodia S (1998) Exploring steganography: seeing the unseen. Computers 31(2):26–34CrossRefGoogle Scholar
  7. 7.
    Nambakhsh MS, Ahmadian A, Zaidi H (2011) A contextual based double watermarking of PET images by patient ID and ECG signal. Comput Methods Programs Biomed 104(3):418–425CrossRefGoogle Scholar
  8. 8.
    Parah SA, Ahad F, Sheikh JA, Bhat GM (2017) Hiding clinical information in medical images: a new high capacity and reversible data hiding technique. J Biomed Inform 66:214–230CrossRefGoogle Scholar
  9. 9.
    Ibaida A, Khalil I, Al-Shammary D (2010) Embedding patients confidential data in ECG signal for healthcare information systems. In: 32nd international conference of IEEE Buenos Aires, Argentina, pp. 3891–3894Google Scholar
  10. 10.
    Ibaida A, Khalil I (2013) Wavelet-based ECG steganography for protecting patient confidential information in point-of-care systems. IEEE Trans Biomed Eng 60(12):3322–3330CrossRefGoogle Scholar
  11. 11.
    Chen ST, Guo YJ, Huang HN, Kung WM, Tseng KK, Tu SY (2014) Hiding patients confidential data in the ECG signal viaa transform-domain quantization scheme. J Med Syst 38(6):1–8Google Scholar
  12. 12.
    Jero SE, Ramu P, Ramakrishnan S (2014) Discrete wavelet transform and singular value decomposition based ECG steganography for secured patient information transmission. J Med Syst 38:132CrossRefGoogle Scholar
  13. 13.
    Jero SE, Ramu P, Ramakrishnan S (2015) ECG steganography using curvelet transform. Biomed Signal Process Control 22:161–169CrossRefGoogle Scholar
  14. 14.
    Jero SE, Ramu P, Ramakrishnan S (2016) Imperceptibility—robustness trade-off studies for ECG steganography using continuous ant colony optimization. Expert Syst Appl 49:123–135CrossRefGoogle Scholar
  15. 15.
    Yang CY, Wang WF (2016) Effective electrocardiogram steganography based on coefficient alignment. J Med Syst 40:66CrossRefGoogle Scholar
  16. 16.
    Pandey A, Saini BS, Singh B, Sood N (2017) An integrated approach using chaotic map & sample value difference method for electrocardiogram steganography and OFDM based secured patient information transmission. J Med Syst 41:187CrossRefGoogle Scholar
  17. 17.
    Malmivuo J, Plonsey R (1995) Bioelectromagnetism: principles and applications of bioelectric and biomagnetic fields. Oxford University Press, New YorkCrossRefGoogle Scholar
  18. 18.
    Poole JE, Singh JP, Birgersdotter-Green U (2016) QRS duration or QRS morphology: what really matters in cardiac resynchronization therapy. J Am Coll Cardiol 67(9):1104–1117CrossRefGoogle Scholar
  19. 19.
    Zhou Y, Bao L, Chang CLP (2014) A new 1D chaotic system for image encryption. Sig Process 97:172–182CrossRefGoogle Scholar
  20. 20.
    Hua Z, Zhou Y, Pun CM, Chang CLP (2015) 2D sine logistic modulation map for image encryption. Inf Sci 297:80–94CrossRefGoogle Scholar
  21. 21.
    Kanso A, Smaoui N (2009) Logistic chaotic maps for binary numbers generations. Chaos Solitons Fractals 40(5):2557–2568CrossRefGoogle Scholar
  22. 22.
    Martínez-González RF, Díaz Méndez JA, Palacios-Luengas L, López-Hernández J, Vázquez-Medina R (2016) A steganographic method using bernoulli’s chaotic maps. Comput Electr Eng 54:435–449CrossRefGoogle Scholar
  23. 23.
    Ravichandran D, Praveenkumar P, Rayappan JBB, Amirtharajan R (2016) Chaos based crossover and mutation for securing DICOM image. Comput Biol Med 72: 170–184CrossRefGoogle Scholar
  24. 24.
    Rukhin A, Soto J, Nechvatal J, Smid M, Barker E et al (2010) A statistical test suite for random and pseudorandom number generators for cryptographic applications. National Institute of Standards and Testing, Washington, DCGoogle Scholar
  25. 25.
  26. 26.
    Pan J, Tompkins WJ (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng BME 32(3):230–236CrossRefGoogle Scholar
  27. 27.
    Slimane ZEH, Naït-Ali A (2010) QRS complex detection using empirical mode decomposition. Digit Signal Proc 20(4):1221–1228CrossRefGoogle Scholar
  28. 28.
    Saini I, Singh D, Khosla A (2013) QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases. J Adv Res 4:331–344CrossRefGoogle Scholar
  29. 29.
    Rodríguez R, Mexicano A, Bila J, Cervantes S, Ponce R (2015) Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. J Appl Res Technol 13:261–269CrossRefGoogle Scholar
  30. 30.
    Carr JJ, Brown JM (2004) Introduction to biomedical equipment technology, 4th edn. Pearson Education, New DelhiGoogle Scholar
  31. 31.
    Yang H, Sun X, Sun G (2009) A high-capacity image data hiding scheme using adaptive LSB substitution. Radio Eng 18(4):509–516Google Scholar
  32. 32.
    Chen J, Itoh S (1998) A wavelet transform-based ECG compression method guaranteeing desired signal quality. IEEE Trans Biomed Eng 45(12):1414–1419CrossRefGoogle Scholar
  33. 33.
    Al-Fahoum AS, Ishijima M (1993) Fundamentals of the decision of optimum factors in the ECG data compression. IEICE Trans Inf Syst E76-D(12):1398–1403Google Scholar
  34. 34.
    Al-Fahoum AS (2006) Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure. IEEE Trans Inf Technol Biomed 10(1):182–191CrossRefGoogle Scholar
  35. 35.
    Manikandan MS, Dandapat S (2007) Wavelet energy based diagnostic distortion measure for ECG. Biomed Signal Process Control 2(2):80–96CrossRefGoogle Scholar
  36. 36.
    Rajaraman V (2016) IEEE standard for floating point numbers. Resonance 21(1):11–30CrossRefGoogle Scholar
  37. 37.
    Pandey A, Singh B, Saini BS, Sood N (2016) A 2D electrocardiogram data compression method using a sample entropy-based complexity sorting approach. Comput Electr Eng 56:36–45CrossRefGoogle Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringDr. B.R. Ambedkar National Institute of TechnologyJalandharIndia
  2. 2.Department of Electronics and Communication EngineeringGuru Nanak Dev UniversityJalandharIndia

Personalised recommendations