Compression of ECG Signal Based on Compressive Sensing and the Extraction of Significant Features

Mohammed M. Abo-Zahhad^1*, Aziza I. Hussein², Abdelfatah M. Mohamed¹

¹Department of Electrical and Electronics Engineering, Faculty of Engineering, Assiut University, Assiut, Egypt.
²Department of Computer and Systems Engineering, Faculty of Engineering, Minia University, Minia, Egypt.

DOI: 10.4236/ijcns.2015.85013                

Abstract

Diagnoses of heart diseases can be done effectively on long term recordings of ECG signals that preserve the signals’ morphologies. In these cases, the volume of the ECG data produced by the monitoring systems grows significantly. To make the mobile healthcare possible, the need for efficient ECG signal compression algorithms to store and/or transmit the signal efficiently has been rising exponentially. Currently, ECG signal is acquired at Nyquist rate or higher, thus introducing redundancies between adjacent heartbeats due to its quasi-periodic structure. Existing compression methods remove these redundancies by achieving compression and facilitate transmission of the patient’s imperative information. Based on the fact that these signals can be approximated by a linear combination of a few coefficients taken from different basis, an alternative new compression scheme based on Compressive Sensing (CS) has been proposed. CS provides a new approach concerned with signal compression and recovery by exploiting the fact that ECG signal can be reconstructed by acquiring a relatively small number of samples in the “sparse” domains through well-developed optimization procedures. In this paper, a single-lead ECG compression method has been proposed based on improving the signal sparisty through the extraction of the signal significant features. The proposed method starts with a preprocessing stage that detects the peaks and periods of the Q, R and S waves of each beat. Then, the QRS-complex for each signal beat is estimated. The estimated QRS-complexes are subtracted from the original ECG signal and the resulting error signal is compressed using the CS technique. Throughout this process, DWT sparsifying dictionaries have been adopted. The performance of the proposed algorithm, in terms of the reconstructed signal quality and compression ratio, is evaluated by adopting DWT spatial domain basis applied to ECG records extracted from the MIT-BIH Arrhythmia Database. The results indicate that average compression ratio of 11:1 with PRD₁ = 1.2% are obtained. Moreover, the quality of the retrieved signal is guaranteed and the compression ratio achieved is an improvement over those obtained by previously reported algorithms. Simulation results suggest that CS should be considered as an acceptable methodology for ECG compression.

Keywords

Compressed Sensing, ECG Signal Compression, Sparsity, Coherence, Spatial Domain

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Addison, P.S. (2005) Wavelet Transforms and the ECG: A Review. Physiological Measurement, 26, R155-R199. http://dx.doi.org/10.1088/0967-3334/26/5/R01
[2]	Abo-Zahhad, M.M., Abdel-Hamid, T.K. and Mohamed, A.M. (2014) Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples. International Journal of Communications, Network and System Sciences, 7, 53-70. http://dx.doi.org/10.4236/ijcns.2014.71007
[3]	Donoho, D.L. (2006) Compressed Sensing. IEEE Transactions on Information Theory, 52, 1289-1306. http://dx.doi.org/10.1109/TIT.2006.871582
[4]	Candes, E., Romberg, J. and Tao, T. (2006) Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information. IEEE Transactions on Information Theory, 52, 489-509. http://dx.doi.org/10.1109/TIT.2005.862083
[5]	Candes, E.J. and Wakin, M.B. (2008) An Introduction to Compressive Sampling. IEEE Signal Processing Magazine, 25, 21-30. http://dx.doi.org/10.1109/MSP.2007.914731
[6]	Mamaghanian, H., Khaled, N., Atienza, D. and Vandergheynst, P. (2011) Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes. IEEE Transactions on Biomedical Engineering, 58, 2456-2466. http://dx.doi.org/10.1109/TBME.2011.2156795
[7]	Chen, F., Chandrakasan, A.P. and Stojanovic, V.M. (2012) Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors. IEEE Journal of Solid-State Circuits, 47, 744-756. http://dx.doi.org/10.1109/JSSC.2011.2179451
[8]	Dixon, A.M.R., Allstot, E.G., Gangopadhyay, D. and Allstot, D.J. (2012) Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors. IEEE Transactions on Biomedical Circuits and Systems, 6, 156-166. http://dx.doi.org/10.1109/TBCAS.2012.2193668
[9]	Polania, L.F., Carrillo, R.E., Blanco-Velasco, M. and Barner, K.E. (2012) Compressive Sensing Exploiting Wavelet Domain Dependencies for ECG Compression. Proceedings of the SPIE Defense, Security, and Sensing, Baltimore, 23-27 April 2012, 83650E.
[10]	Polania, L.F., Carrillo, R.E., Blanco-Velasco, M. and Barner, K.E. (2012) On Exploiting Interbeat Correlation in Compressive Sensing-Based ECG Compression. Proceedings of the SPIE Defense, Security, and Sensing, Baltimore, 23-27 April 2012, 83650D
[11]	Polania, L.F., Carrillo, R.E., Blanco-Velasco, M. and Barner, K.E. (2012) Compressive Sensing for ECG Signals in the Presence of Electromyography Noise. Proceedings of the 38th Annual Northeast Bioengineering Conference (NEBEC), Philadelphia, 16-18 March 2012, 295-296. http://dx.doi.org/10.1109/NEBC.2012.6207081
[12]	Zhang, Z., Jung, T., Makeig, S. and Rao, B.D. (2013) Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Non-Invasive Fetal ECG via Block Sparse Bayesian Learning. IEEE Transactions on Biomedical Engineering, 60, 300-309. http://dx.doi.org/10.1109/TBME.2012.2226175
[13]	Polanía, L.F., Carrillo, R.E., Blanco-Velasco, M. and Barner, K.E. (2011) ECG Compression via Matrix Completion. Proceedings of the European Signal Processing Conference, Barcelona, 29 August-3 September 2011, 1-5.
[14]	Candes, E.J. and Tao, T. (2005) Decoding by Linear Programming. IEEE Transactions on Information Theory, 51, 4203-4215. http://dx.doi.org/10.1109/TIT.2005.858979
[15]	Polania, L.F., Carrillo, R.E., Blanco-Velasco, M. and Barner, K.E. (2011) Compressed Sensing Based Method for ECG Compression. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, 22-27 May 2011, 761-764.
[16]	Candes, E. and Tao, T. (2006) Near-Optimal Signal Recovery from Random Projections: Universal Encoding Strategies? IEEE Transactions on Information Theory, 52, 5406-5425. http://dx.doi.org/10.1109/TIT.2006.885507
[17]	Calderbank, R., Howard, S. and Jafarpour, S. (2010) Construction of a Large Class of Deterministic Sensing Matrices That Satisfy a Statistical Isometry Property. IEEE Journal on Selected Topics in Signal Processing, 4, 358-374. http://dx.doi.org/10.1109/JSTSP.2010.2043161
[18]	Amini, A., Montazerhodjat, V. and Marvasti, F. (2012) Matrices with Small Coherence Using Parry Block Codes. IEEE Transactions on Signal Processing, 60, 172-181. http://dx.doi.org/10.1109/TSP.2011.2169249
[19]	Nguyen, T.L. and Shin, Y. (2013) Deterministic Sensing Matrices in Compressive Sensing: A Survey. The Scientific World Journal, 2013, 1-6.
[20]	Candes, E.J. and Romberg, J. (2015) l1-MAGIC: Recovery of Sparse Signals via Convex Programming. http://www.acm.caltech.edu/l1magic
[21]	Boyd, S. and Vandenberghe, L. (2009) Convex Optimization. 7th Edition, Cambridge University Press, Cambridge. http://www.stanford.edu/~boyd/cvxbook/ http://dx.doi.org/10.1017/CBO9780511804441
[22]	Natarajan, B.K. (1995) Sparse Approximate Solutions to Linear Systems. SIAM Journal on Computing, 24, 227-234. http://dx.doi.org/10.1137/S0097539792240406
[23]	Mallat, S.G. and Zhang, Z. (1993) Matching Pursuits with Time-Frequency Dictionaries. IEEE Transactions on Signal Processing, 41, 3397-3415. http://dx.doi.org/10.1109/78.258082
[24]	Chen, S.S., Donoho, D.L. and Saunders, M.A. (2001) Atomic Decomposition by Basis Pursuit. SIAM Review, 43, 129-159. http://dx.doi.org/10.1137/S003614450037906X
[25]	Bioucas-Dias, J. and Figueiredo, M. (2007) A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration. IEEE Transactions on Image Processing, 16, 2992-3004. http://dx.doi.org/10.1109/TIP.2007.909319
[26]	Pati, Y.C., Rezaiifar, R. and Krishnaprasad, P.S. (1993) Orthogonal Matching Pursuit: Recursive Function Approximation with Applications to Wavelet Decomposition. Proceedings of the 27th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, 1-3 November 1993, 40-44. http://dx.doi.org/10.1109/ACSSC.1993.342465
[27]	Grant, M. and Boyd, S. (2008) CVX: Matlab Software for Disciplined Convex Programming. http://stanford.edu/boyd/cvx
[28]	Chambolle, A. and Lions, P.L. (1997) Image Recovery via Total Variation Minimization and Related Problems. Numerische Mathematik, 76, 167-188. http://dx.doi.org/10.1007/s002110050258
[29]	Chambolle, A. (2004) An Algorithm for Total Variation Minimization and Applications. Journal of Mathematical Imaging and Vision, 20, 89-97. http://dx.doi.org/10.1023/B:JMIV.0000011321.19549.88
[30]	Singh, B.N. and Tiwari, A.K. (2006) Optimal Selection of Wavelet Basis Function Applied to ECG Signal Denoising. Digital Signal Processing, 16, 275-287. http://dx.doi.org/10.1016/j.dsp.2005.12.003
[31]	Besar, R., Eswaran, C., Sahib, S. and Simpson, R.J. (2000) On the Choice of the Wavelets for ECG Data Compression. Proceedings of the 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, Istanbul, 4-6 June 2000, 3614-3617. http://dx.doi.org/10.1109/ICASSP.2000.860184
[32]	Roy, A.B., Dey, D., Mohanty, B. and Banerjee, D. (2012) Comparison of FFT, DCT, DWT, WHT Compression Techniques on Electrocardiogram and Photoplethysmography Signals. IJCA Special Issue on International Conference on Computing, Communication and Sensor Network CCSN, 2012, 6-11.
[33]	Thakor, N.V., Webster, J.G. and Tompkins, W.J. (1983) Optimal QRS Detector. Medical Biological Engineering Computing, 21, 343-250.
[34]	Okada, M. (1979) A Digital Filter for the QRS Complex Detection. Transactions on Biomedical Engineering, 26, 700-703.
[35]	MIT-BIH Arrhythmia Database, 2014. www. physionet.org/physiobank/database/mitdb
[36]	Lu, Z., Kim, D.Y. and Pearlman, W.A. (2000) Wavelet Compression of ECG Signals by the Set Partitioning in Hierarchical Trees Algorithm. IEEE Transactions on Biomedical Engineering, 47, 849-856. http://dx.doi.org/10.1109/10.846678
[37]	Zigel, Y., Cohen, A. and Katz, A. (2000) The Weighted Diagnostic Distortion (WDD) Measure for ECG Signal Compression. IEEE Transactions on Biomedical Engineering, 47, 1422-1430. http://dx.doi.org/10.1109/TBME.2000.880093