Heart Sound (PCG) Signal Compression based on Down-sampling and two-dimensionalization

Document Type : Original Article

Authors

1 Shahrood University of Technology

2 Department of Electrical and Robotics Engineering, Shahrood, Iran

Abstract

In this paper, a lossy compression method with the ability to control the quality of the reconstructed signal is proposed for phonocardiography (PCG) signals. It is based on two main ideas: down-sampling and two-dimensionalization. For PCG image compression, wavelet transform and Spatial-oriented Tree Wavelet (STW) encoder are used. In the proposed method, there is the ability to control the quality of the reconstructed signal using a Percent Root-mean-square Difference (PRD)-related threshold. The simulation results of the proposed method on some public databases indicates that the down-sampling stage has a significant effect on increasing the compression ratio especially in the case of databases with high sampling frequency. The next important factor in improving the compression efficiency of the proposed method is the use of two-dimensional PCG signal in order to take advantage of the inter-period redundancy in this type of repetitive signals, and using modern effective methods for image compression. The efficiency of the proposed method was evaluated according to the average PRD and Compression Ratio (CR) criteria and compared with the results of several existing methods. In this evaluation, while limiting PRD≤5%, the lowest average compression ratio was related to the Artifacts dataset from the Pascal database (with a sampling frequency of 2000 Hz) and the highest average compression ratio was related to the database of the University of Washington (with a sampling frequency of 44100 Hz).

Keywords

References

[1] Y.-R. Chien, K.-Ch. Hsu, and H.-W. Tsao, "Phonocardiography signals compression with deep convolutional autoencoder for telecare applications," Applied Sciences, vol. 10, no. 17, p. 5842, 2020.
[2] A. Subasi, Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach, Academic Press, 2019.
[3] N. Boukhennoufa, K. Benmahammed and R. Benzid, “Effective PCG signals compression technique using an enhanced 1-D EZW,” International Journal of Advanced Science and Technology, vol. 48, pp. 89-102, 2012.
[4] M. S. Manikandan and S. Dandapat, " Wavelet-based ECG and PCG signals compression technique for mobile telemedicine," 15th International Conference on Advanced Computing and Communications (ADCOM 2007), pp. 164-169, Guwahati, India, 18-21 Dec. 2007.
[5] S. Kim and D. Hwang, “Murmur-adaptive compression technique for phonocardiogram signals,” IEEE Electronics Letters, vol. 52, no. 3, pp. 183-184, 2016.
[6] A. Bendifallah, M. Boulemden and R. Benzid, “Bitmask and SPIHT based PCG signal compression”, 2015 4th International Conference on Electrical Engineering (ICEE), 13-15 Dec. 2015, Boumerdes, Algeria.
[7] M. Chowdhury, K. Poudel, and Y. Hu, " Phonocardiography data compression using discrete wavelet transform,"  2018 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA, 1-1 Dec. 2018.
[8] M. S. Manikandan, K. P. Soman, and S. Dandapat, " Quality-driven wavelet based PCG signal coding for wireless cardiac patient monitoring," Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian(ACWR 2011), pp. 519-526, Amritapuri, December 2011.
[9] J. M. Aljarin and R. R. Merino, "Wavelet and wavelet packet compression of phonocardiograms," Electronic Letters, vol. 40, no. 17, pp. 1040-1041, 2004.
[10] V. Aggarwal, S. Gupta, M. S. Patterh, and L. Kaur, " Analysis of compressed foetal phonocardiography (PCG) signals with discrete cosine transform and discrete wavelet transform," IETE Journal of Research, doi: 10.1080/03772063.2020.1725662, 2020.
[11] W. Qin and P. Wang, "A remote heart sound monitoring system based on LZSS lossless compression algorithm,"  2013 IEEE 4th International Conference on Electronics Information and Emergency Communication, Beijing, China, 15-17 Nov. 2013.
[12] H. Tang, J. Zhang, J. Sun, T. Qiu, and Y. Park, "Phonocardiogram signal compression using sound repetition and vector quantization," Computers in Biology and Medicine, vol. 71, pp. 24-34, 2016.
[13] J. M.- Alajarin, J. L.- Candel, and R. R.-Merino, " ASEPTIC: Aided system for event-based phonocardiographic telediagnosis with integrated compression," 2006 Computers in Cardiology, Valencia, Spain, 17-20 Sept. 2006.
[14] J. M.- Alajarin, J. G.- Guerrero, and R. R.- Merino, " Optimization of the compression parameters of a phonocardiographic telediagnosis system using genetic algorithms,"In: Mira J., Álvarez J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC pp. 508–517 June 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg.
[15] S. Patidar and R. B. Pachori, "Tunable-Q wavelet transform based optimal compression of cardiac sound signals," 2016 IEEE Region 10 Conference (TENCON), 2016, pp. 2193-2197, doi: 10.1109/TENCON.2016.7848416.
[16] J. M. Shapiro, "Embedded image coding using zerotrees of wavelet coefficients," IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3445–3462, 1993.
[17] A. Said and W. A. Pearlman, "A new, fast, and efficient image codec based on set partitioning in hierarchical trees" IEEE Transactions on Circuits Systems and Video Technology, vol. 6, no. 3, pp. 243–250, 1996.
[18] A. Said and W. A. Pearlman, "Image compression using the spatial-orientation tree," in Proceedings of the IEEE International Symposium Circuits and Systems, pp. 279–282, 1993.
[19] W. Burger and M. J. Burge, Principles of digital image processing: core algorithms, Springer, London, 2009.
[20] http://www.med.umich.edu/lrc/psb_open/html/repo/primer_heartsound/primer_heartsound.html. Accessed 1 Apr 2021.
[21] https://physionet.org/content/fpcgdb/1.0.0/. Accessed 1 Apr 2021.
[22] P Bentley, PASCAL Classifying heart sounds challenge (2011). http://www. peterjbentley.com/heartchallenge/ Accessed 1 Apr 2021.
[23] https://physionet.org/content/challenge-2016/1.0.0/ Accessed 1 Apr 2021.
[24] http://depts.washington.edu/physdx/heart/demo.htmlAccessed 1 Apr 2021.
[25] M. Chowdhury, K. Poudel, and Y. Hu, "Automatic phonocardiography analysis using discrete wavelet transform," Proceedings of the 3rd International Conference on Vision, Image and Signal Processing (ICVISP), Vancouver, BC, Canada, 26-28 August, 2019. 
Advanced Signal Processing
Volume 4, Issue 2 - Serial Number 6
December 2020
Pages 263-278

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