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ECG monitoring facilitates in providing accelerated health status of the concerned patient to the healthcare centre in case of the hostile cardiac behavior. For continuous monitoring of patient’s health, the ECG signal is recorded for long time durations typically for several hours or few days. The storage of such massive volume of data requires large memory space. To combat with such a huge growth rate of memory requirement and data sparsity, many ECG compression and de-compression algorithm had already developed to represent the raw ECG in the processed format. In this paper, the review of approaches dealing with ECG data compression is presented. It also highlights the research challenges relevant to each domain.
• R. U. Acharya, J. S. Suri, J. A. . Spaan, and S. . M. Krishnan, Advances in Cardiac Signal Processing, 1st ed. Springer, 2007.
• M. Nichols, N. Townsend, P. Scarborough, and M. Rayner, “Cardiovascular disease in Europe: Epidemiological update,” Eur. Heart J., vol. 35, no. 42, pp. 3028– 3034, 2014.
• S. Gupta, R. Gudapati, K. Gaurav, and M. Bhise, “Emerging risk factors for cardiovascular diseases: Indian context,” Indian J. Endocrinol. Metab., vol. 17, no. 5, pp. 806–814, 2013.
• Á. Alesanco and J. García, “Clinical assessment of wireless ECG transmission in real-time cardiac telemonitoring,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 5, pp. 1144–1152, 2010.
• S. S. Mahmoud, Q. Fang, Z. M. Hussain, and I. Cosic, “A blind equalization algorithm for biological signals transmission,” Digit. Signal Process. A Rev. J., vol. 22, no. 1, pp. 114–123, 2012.
• A. Ibaida, D. Al-Shammary, and I. Khalil, “Cloud enabled fractal based ECG compression in wireless body sensor networks,” Futur. Gener. Comput. Syst., vol. 35, pp. 91–101, 2014.
• Á. Alesanco and J. García, “Automatic real-time ECG coding methodology guaranteeing signal interpretation quality,” IEEE Trans. Biomed. Eng., vol. 55, no. 11, pp. 2519–2527, 2008.
• M. Sabarimalai Sur and S. Dandapat, “Wavelet-based electrocardiogram signal compression methods and their performances: A prospective review,” Biomed. Signal Process. Control, vol. 14, no. 1, pp. 73–107, 2014.
• K. Sayood, Introduction to Data Compression, vol. 3. Newnes, 2012.
• A. Koski, “Lossless ECG encoding,” Comput. Methods Programs Biomed., vol. 52, no. 1, pp. 23–33, 1997.
• E. B. L. Filho, N. M. M. Rodrigues, E. Silva, S. Faria, E. A. B. Da Silva, S. M. M. De Faria, V. M. M. Da Silva, and M. B. De Carvalho, “ECG signal compression based on dc equalization and complexity sorting,” IEEE Trans. Biomed. Eng., vol. 55, no. 7, pp. 1923–1926, 2008.
• H. H. Chou, Y. J. Chen, Y. C. Shiau, and T. S. Kuo, “An effective and efficient compression algorithm for ECG signals with irregular periods,” IEEE Trans. Biomed. Eng., vol. 53, no. 6, pp. 1198–1205, 2006.
• X. Wang, J. Meng, S.-C. Tai, C.-C. Sun, and W.- C. Yan, “A 2-D ECG compression algorithm based on wavelet transform and Modified SPIHT,” IEEE Trans. Biomed. Eng., vol. 52, no. 6, pp. 999–1008, Mar. 2008.
• J. Wei, S. Member, C. Chang, N. Chou, and G. Jan, “ECG Data Compression Using Truncated Singular Value Decomposition,” vol. 5, no. 4, pp. 290–299, 2001.
• H. Lee and K. M. Buckley, “ECG Data Compression Using Cut and Align Beats Approach and 2-D Transforms,” vol. 46, no. 5, pp. 556–564, 1999.
• A. Ibaida and I. Khalil, “Wavelet-based ECG steganography for protecting patient confidential information in point-of-care systems,” IEEE Trans. Biomed. Eng., vol. 60, no. 12, pp. 3322–3330, 2013.
• W. B. Lee and C. D. Lee, “A cryptographic key management solution for HIPAA privacy/security regulations,” IEEE Trans. Inf. Technol. Biomed., vol. 12, no. 1, pp. 34–41, 2008.
• S. K. Chen, T. Kao, C. T. Chan, C. N. Huang, C. Y. Chiang, C. Y. Lai, T. H. Tung, and P. C. Wang, “A reliable transmission protocol for zigbee-based wireless patient monitoring,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 1, pp. 6–16, 2012.
• A. Ligata, H. Gacanin, F. Adachi, M. Smolnikar, and M. Mohorcic, “Bit error rate analysis for an OFDM system with channel estimation in a nonlinear and frequency-selective fading channel,” Eurasip J. Wirel. Commun. Netw., vol. 2011, 2011.
• K. N. Le and K. P. Dabke, “BER of OFDM with diversity and pulse shaping in Rayleigh fading environments,” Digit. Signal Process., vol. 20, no. 6, pp. 1687–1696, 2010.
• L. Rugini and P. Banelli, “BER of OFDM systems impaired by carrier frequency offset in multipath fading channels,” IEEE Trans. Wirel. Commun., vol. 4, no. 5, pp. 2279–2288, 2005.
• K. Sathananthan and C. Tellambura, “Probability of Error Calculation of OFDM Systems With Frequency Offset,” vol. 49, no. 11, pp. 1884–1888, 2001.
• S. M. Jalaleddine, C. G. Hutchens, R. D. Strattan, and W. a Coberly, “ECG data compression techniques–a unified approach.,” Biomed. Eng. IEEE Trans., vol. 37, no. 4, pp. 329–43, 1990.
• J. R. Cox, F. M. Nolle, H. A. Fozzard, and G. C. Oliver, “AZTEC, a preprocessing program for real-time ECG rhythm analysis,” IEEE Trans. Biomed. Eng., vol. 15, no. April, pp. 128–129, 1968.
• V. Kumar, “Improved modified AZTEC technique for ECG data compression : Effect of length of parabolic filter on reconstructed signal,” Comput. Electr. Eng., vol. 31, pp. 334–344, 2005.
• V. Kumar, S. C. Saxena, and V. K. Giri, “Direct data compression of ECG signal for telemedicine,” Int. J. Syst. Sci., vol. 37, no. 1, pp. 45–63, 2006.
• S. K. Mukhopadhyay, S. Mitra, and M. Mitra, “A lossless ECG data compression technique using ASCII character encoding,” Comput. Electr. Eng., vol. 37, no. 4, pp. 486–497, 2011. [28] 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.
• S. K. Mukhopadhyay, S. Mitra, and M. Mitra, “ECG signal compression using ASCII character encoding and transmission via SMS,” Biomed. Signal Process. Control, vol. 8, no. 4, pp. 354–363, 2013.
• B. R. S. Reddy and I. S. N. Murthy, “ECG data compression using Fourier descriptors,” IEEE Trans. Biomed. Eng., vol. 33, no. 4, pp. 428–434, 1986.
• L. V. Batista, E. Uwe, K. Melcher, and L. C. Carvalho, “Compression of ECG signals by optimized quantization of discrete cosine transform coefficients,” vol. 23, pp. 127–134, 2001.
• 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.
• Z. Lu, D. Y. Kim, and W. A. Pearlman, “Wavelet Compression of ECG Signals by the Set Partitioning in Hierarchical Trees Algorithm,” IEEE Trans. Biomed. Eng., vol. 47, no. 7, pp. 849–856, 2000.
• X. Wang and J. Meng, “A 2-D ECG compression algorithm based on wavelet transform and vector quantization,” Digit. Signal Process., vol. 18, pp. 179–188, 2008.
• S. C. Tai, C. C. Sun, and W. C. Yan, “A 2-D ECG compression method based on wavelet transform and modified SPIHT,” IEEE Trans. Biomed. Eng., vol. 52, no. 6, pp. 999–1008, 2005.
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