An Enhanced Lossless Compression with Cryptography Hybrid Mechanism for ECG Biomedical Signal Monitoring

Mustafa Emad Hameed


Due to their use in daily life situation, demand for remote health applications and e-health monitoring equipment is growing quickly. In this phase, for fast diagnosis and therapy, information can be transferred from the patient to the distant clinic. Nowadays, the most chronic disease is cardiovascular diseases (CVDs). However, the storage and transmission of the ECG signal, consumes more energy, bandwidth and data security which is faced many challenges. Hence, in this work, we present a combined approach for ECG data compression and cryptography. The compression is performed using adaptive Huffman encoding and encrypting is done using AES (CBC) scheme with a 256-bit key. To increase the security, we include Diffie-Hellman Key exchange to authenticate the receiver, RSA key generation for encrypting and decrypting the data. Experimental results show that the proposed approach achieves better performance in terms of compression and encryption on MIT-BIH ECG dataset.


Adaptive Huffman Coding, Compression, ECG Signal, Encryption, Security


E. Yulianto, A. Susanto, T. S. Widodo, and S. Wibowo, “Classifying the EEG Signal through Stimulus of Motor Movement Using New Type of Wavelet,” IAES Int. J. Artif. Intell., vol. 1, no. 3, pp. 139–148, 2012.

J. S. Sahambi, S. N. Tandon, and R. K. P. Bhatt, “Using wavelet transforms for ECG characterization. An on-line digital signal processing system,” IEEE Eng. Med. Biol. Mag., vol. 16, no. 1, pp. 77–83, 1997.

A. Alesanco and J.Garc´ıa, “Automatic Real-Time ECG Coding Methodology Guaranteeing Signal Interpretation Quality,” IEEE Trans. Biomed. Eng., vol. 55, no. 11, pp. 2519–2527, 2008.

D.M. Mirris and A.L. Goldberger, "Braunwald: Heart Disease: A Textbook of Cardiovascular Medicine", 6th ed., Copyright © 2001 W. B. Saunders Company, 6th ed. 2001.

H. Kupwade Patil and R. Seshadri, “Big Data Security and Privacy Issues in Healthcare,” 2014 IEEE Int. Congr. Big Data, pp. 762–765, 2014.

K. Gai, Y. Wu, L. Zhu, and M. Qiu, “Privacy-preserving Energy Trading Using Consortium Blockchain in Smart Grid,” IEEE Trans. Ind. Informatics, vol. 15, no. 6, pp. 3548–3558, 2019.

J. Andreu-Perez, C. C. Y. Poon, R. D. Merrifield, S. T. C. Wong, and G.-Z. Yang, “Big Data for Health,” IEEE J. Biomed. Heal. Informatics, vol. 19, no. 4, pp. 1193–1208, 2015.

S. K. Mukhopadhyay, S. Mitra, and M. Mitra, “An ECG signal compression technique using ASCII character encoding,” Measurement, vol. 45, no. 6, pp. 1651–1660, 2012.

M. K. Abdulhameed, Z. Zakaria, I. Ibrahim, and M. K. Mohsen, “Novel design of triple-band EBG Novel design of triple-band EBG,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 17, no. 4, pp. 1683–1691, 2019.

D. Gurve, B. S. Saini, and I. Saini, “An improved lossy and lossless combined ECG data compression using ASCII character encoding,” Int. J. Med. Eng. Inform., vol. 8, no. 4, pp. 758–764, 2016.

A. F. Hussein, S. J. Hashim, A. F. A. Aziz, F. Z. Rokhani, and W. A. W. Adnan, “A real time ECG data compression scheme for enhanced bluetooth low energy ECG system power consumption,” J. Ambient Intell. Humaniz. Comput., p. 1–14, 2017.

K. Gai, M. Qiu, Y. Li, and X. Liu, “Advanced Fully Homomorphic Encryption Scheme Over Real Numbers,” 4th International Conference on Cyber Security and Cloud Computing IEEE, pp. 64–69, 2017.

H. Al-Hamadi, A. Gawanmeh, J. Baek, and M. Al-Qutayri, “Lightweight Security Protocol for ECG Bio-Sensors,” Wirel. Pers. Commun., vol. 95, no. 4, pp. 5097–5120, 2017.

M. Fira, “Applications of compressed sensing: Compression and encryption,” in 2015 E-Health and Bioengineering Conference (EHB), 2015, pp. 1–4.

J. Ma, T. Zhang, and M. Dong, “A novel ECG data compression method using adaptive fourier decomposition with security guarantee in e-health applications,” IEEE J. Biomed. Heal. Informatics, vol. 19, no. 3, pp. 986–994, 2015.

M. Raeiatibanadkooki and S. R. Quchani, “Compression and Encryption of ECG Signal Using Wavelet and Chaotically Huffman Code in Telemedicine Application,” Mob. Syst., vol. 40, no. 3, pp. 1-8, 2016.

T. Y. Liu, K. J. Lin, and H. C. Wu, “ECG data encryption then compression using singular value decomposition,” IEEE J. Biomed. Heal. Informatics, vol. 22, no. 3, pp. 707–713, 2018.

A. Pandey, B. S. Saini, B. Singh, and N. Sood, "Complexity sorting and coupled chaotic map based on 2D ECG data compression-then-encryption and its OFDM transmission with impair sample correction", Multimedia Tools and Applications, vol.78, no. 9, pp. 11223–11261, 2019.

N. A. M. Mustafa Emad Hameed, Masrullizam Mat Ibrahim, “Compression and Encryption for ECG Biomedical Signal in Healthcare System,” TELKOMNIKA, vol. 17, no. 6, 2019.

M. E. Hameed, M. M. Ibrahim, and N. A. Manap, “Review on Improvement of Advanced Encryption Standard ( AES ) Algorithm based on Time Execution , Differential Cryptanalysis and Level of Security,” J. Telecommun. Electron. Comput. Eng., vol. 10, no. 1, pp. 139–145, 2018.

M. L. A. Mustafa Emad Hameed, Masrullizam Mat Ibrahim, Nurulfajar Abd Manap, “Comparative study of several operation modes of AES algorithm for encryption ECG biomedical signal,” Int. J. Electr. Comput. Eng., vol. 9, no. 6, 2019.

M. Vaidehi and B. J. Rabi, “Design and analysis of AES-CBC mode for high security applications,” 2nd Int. Conf. Curr. Trends Eng. Technol. ICCTET 2014, pp. 499–502, 2014.

E. R. Arboleda, J. L. Balaba, and J. C. L. Espineli, “Chaotic rivest-shamir-adlerman algorithm with data encryption standard scheduling,” Bull. Electr. Eng. Informatics, vol. 6, no. 3, pp. 219–227, 2017.

S. Lee, J. Kim, and M. Lee, “A real-time ECG data compression and transmission algorithm for an e-health device,” IEEE Trans. Biomed. Eng., vol. 58, no. 9, pp. 2448–2455, 2011.

A. Singh, L. N. Sharma, and S. Dandapat, “Multi-channel ECG data compression using compressed sensing in eigenspace,” Comput. Biol. Med., vol. 73, pp. 24–37, 2016.

H. Anas, R. Latif, and M. Arioua, “Efficient electrocardiogram (ECG) lossy compression scheme for real time e-Health monitoring,” Int. J. Biol. Biomed. Eng., vol. 11, pp. 101–114, 2017.

O. Yildirim, R. S. Tan, and U. R. Acharya, “An efficient compression of ECG signals using deep convolutional autoencoders,” Cogn. Syst. Res., vol. 52, pp. 198–211, 2018.

H. Kim, R. F. Yazicioglu, P. Merken, C. Van Hoof, and H. J. Yoo, “ECG signal compression and classification algorithm with quad level vector for ECG holter system,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 1, pp. 93–100, 2010.

M. Burhanuddin, A. Mohammed, R. Ismail, M. E. Hameed, A. N. Kareem, and H. Basiron, “A Review on Security Challenges and Features in Wireless Sensor Networks: IoT Perspective,” J. Telecommun. Electron. Comput. Eng., vol. 10, no. 1–7, pp. 17–21, 2018.

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