Investigation on Daubechies Wavelet-Based Compressed Sensing Matrices for ECG Compression

In this paper, we have investigated the different Daubechies (DB) wavelet-based compressed sensing (CS) matrices, namely db3, db4, db5, db6, db7, db8, db9, and db10 measurement matrices for ECG compression. The performance of the proposed Daubechies wavelet-based measurement matrices and state-of-the-art measurement matrices are evaluated using different performance measures such as Compression Ratio (CR), PRD, SNR, RMSE, and signal reconstruction time. The result demonstrates that the db3 and db10 measurement matrices outperform the state-of-the-art measurement matrices. Moreover, db3 and db4 measurement matrices show superior performance compared to db4, db5, db6, db7, db8, and db9 measurement matrices. Thus, this study exhibits the successful implementation of Daubechies (DB) wavelet-based sensing matrices for ECG compression.

[1]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[2]  Manuel Blanco-Velasco,et al.  Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems , 2014, IEEE Journal of Biomedical and Health Informatics.

[3]  M. Lustig,et al.  Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.

[4]  A. Mishra,et al.  Selecting the Most Favorable Wavelet for Compressing ECG Signals Using Compressive Sensing Approach , 2012, 2012 International Conference on Communication Systems and Network Technologies.

[5]  Salman Durrani,et al.  Performance study of compressive sampling for ECG signal compression in noisy and varying sparsity acquisition , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Sanjay L. Nalbalwar,et al.  Application of 1-D discrete wavelet transform based compressed sensing matrices for speech compression , 2016, SpringerPlus.

[7]  Tzyy-Ping Jung,et al.  Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning , 2012, IEEE Transactions on Biomedical Engineering.

[8]  D. Donoho,et al.  Atomic Decomposition by Basis Pursuit , 2001 .

[9]  Yulong Gao,et al.  Advances in Theory of Compressive Sensing and Applications in Communication , 2011, 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control.

[10]  D. L. Donoho,et al.  Compressed sensing , 2006, IEEE Trans. Inf. Theory.

[11]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[12]  Richard G. Baraniuk,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[13]  Manuel Blanco-Velasco,et al.  Compressed sensing based method for ECG compression , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  S. Hosseini-Khayat,et al.  ECG signal compression using compressed sensing with nonuniform binary matrices , 2012, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012).

[15]  Falgun Thakkar,et al.  ECG signal compression using Compressive Sensing and wavelet transform , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Sanjay L. Nalbalwar,et al.  Application of Compressed Sensing (CS) for ECG Signal Compression: A Review , 2017 .