An Integrated Approach for ECG Compression and Encryption in E-healthcare Systems
Keywords:
ECG Compression, Encryption, Adaptive Fourier Transform (AFD), Combined Chaotic Map CCM), Compression Ratio (CR), Quality Score(QS)
Abstract
The recent developments in tele-healthcare services has raised the concern over the management and security of biomedical data during its transmission and storage. This paper proposes an integrated approach of signal compression and encryption to resolve these issues. Signal compression is done using Adaptive Fourier Decomposition (AFD) technique that decompose the Electrocardiogram (ECG) signal in terms of adaptively selected Basis functions instead of fixed Basis functions used in other decomposition techniques. Further chaotic maps are employed to encrypt the AFD coefficients. The performance of the integrated approach is evaluated in terms of distortion parameters such as PRD, PSNR and SNR while Compression Ratio (CR) and Quality score are used to measure compression efficiency. The results show that the proposed technique is efficient as compared to the state of the art techniques.
References
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[9] Feli, M., Abdali-Mohammad, F.: ‘12 lead electrocardiography signals compression by a new genetic programming based mathematical modeling algorithm’, Biomedical Signal Processing and Control, 2019, 54, 101596, pp. 1-11
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[11] Zhai, X., Ali, A.A.S., Amira, A., Bensaali, F.: ‘ECG encryption and identification based security solution on the Zynq SoC for connected health systems’, Journal of Parallel and Distributed Computing, 2017, 106, pp. 143–152
[12] Qian, T., Zhang, L., Li, Z.: ‘Algorithm of adaptive Fourier decomposition’, IEEE Transactions on Signal Processing, 2011, 59, (12), pp. 5899-5906
[13] Qian, T., Li, H., Stessin, M.: ‘Comparison of adaptive mono-component decompositions’, Nonlinear Analysis: Real World Applications, 2013, 14, (2), pp. 1055-1074
[14] Soni, N., Saini, I., Singh, B.: ‘A morphologically robust chaotic map based approach to embed patient’s confidential data securely in non-QRS regions of ECG signal’, Australasian Physical & Engineering Sciences in Medicine, 2019, 42, (1), pp. 111-135
[15] Hua, Z., Zhou, Y., Pun, C.M., et al.: ‘2D sine logistic modulation map for image encryption’, Information Sciences, 2015, 297, pp. 80-94
[16] Rajaraman, V.: ‘IEEE standard for floating point numbers’, Resonance, 2016, 21, (Laszlo, A., & Castro, K. (1995). Technology and values: Interactive learning environments for future generations. Educational Technology, 35(2), 7-13.
[17] AFD and chaotic map-based integrated approach for ECG compression, steganography and encryption in E-healthcare paradigm, IET Signal Processing, vol. 15, Issue 5, pp. 337 - 351, 2021
[18] www.physionet.org/cgi-bin/atm/ATM
[2] Seera, M., Lim, C.P., Liew, W.S.,et al.: ‘Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models’, Expert Systems with Applications, 2015, 42, (7), pp. 3643–3652
[3] Algeria-Barrero, E., Algeria-Ezquerra, E.: ‘When to perform pre-operative ECG’, E-journal of Cardiology Practice, 2008, 7, (13)
[4] Pandey, A., Saini, B.S., Singh, B., et al.: ‘A 2D electrocardiogram data compression method using a sample entropy-based complexity sorting approach,’ Computers and Electrical Engineering, 2016, 56, pp. 36-45
[5] Mukhopadhyay, S.K., Mitra, S., Mitra, M.: ‘ECG signal compression using ASCII character encoding and transmission via SMS’, Biomedical Signal Processing and Control, 2013, 8, (4), pp. 354–363
[6] Ma, J.L., Zhang, T.T., Dong, M.‘A novel ECG data compression method using adaptive Fourier decomposition with security guarantee in e-health applications’, IEEE Journal of Biomedical and Health Informatics, 2015, 19, (3), pp. 986-994
[7] Jha, C.K., Kolekar, M.H.: ‘Electrocardiogram data compression using DCT based discrete orthogonal stockwell transform’, Biomedical Signal Processing and Control, 2018, 46, pp. 174-181
[8] Chandra, S., Sharma, A., Singh, G.K.: ‘Computationally efficient cosine modulated filter bank design for ECG signal compression’, IRBM, 2020, 41, (1), pp. 2-17
[9] Feli, M., Abdali-Mohammad, F.: ‘12 lead electrocardiography signals compression by a new genetic programming based mathematical modeling algorithm’, Biomedical Signal Processing and Control, 2019, 54, 101596, pp. 1-11
[10] Mathivanan, P., Ganesh, A.B., Venkatesan, R.: ‘QR code–based ECG signal encryption/decryption algorithm’, Cryptologia, 2019, 43, (3), pp. 233-253
[11] Zhai, X., Ali, A.A.S., Amira, A., Bensaali, F.: ‘ECG encryption and identification based security solution on the Zynq SoC for connected health systems’, Journal of Parallel and Distributed Computing, 2017, 106, pp. 143–152
[12] Qian, T., Zhang, L., Li, Z.: ‘Algorithm of adaptive Fourier decomposition’, IEEE Transactions on Signal Processing, 2011, 59, (12), pp. 5899-5906
[13] Qian, T., Li, H., Stessin, M.: ‘Comparison of adaptive mono-component decompositions’, Nonlinear Analysis: Real World Applications, 2013, 14, (2), pp. 1055-1074
[14] Soni, N., Saini, I., Singh, B.: ‘A morphologically robust chaotic map based approach to embed patient’s confidential data securely in non-QRS regions of ECG signal’, Australasian Physical & Engineering Sciences in Medicine, 2019, 42, (1), pp. 111-135
[15] Hua, Z., Zhou, Y., Pun, C.M., et al.: ‘2D sine logistic modulation map for image encryption’, Information Sciences, 2015, 297, pp. 80-94
[16] Rajaraman, V.: ‘IEEE standard for floating point numbers’, Resonance, 2016, 21, (Laszlo, A., & Castro, K. (1995). Technology and values: Interactive learning environments for future generations. Educational Technology, 35(2), 7-13.
[17] AFD and chaotic map-based integrated approach for ECG compression, steganography and encryption in E-healthcare paradigm, IET Signal Processing, vol. 15, Issue 5, pp. 337 - 351, 2021
[18] www.physionet.org/cgi-bin/atm/ATM
Published
2022-03-26
How to Cite
Neetika Soni, & Vinit Grewal. (2022). An Integrated Approach for ECG Compression and Encryption in E-healthcare Systems. Revista Electronica De Veterinaria, 580-585. https://doi.org/10.69980/redvet.vi.1890
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