Contribution to S-EMG Signal Compression in 1D by the Combination of the Modified Discrete Wavelet Packet Transform (MDWPT) and the Discrete Cosine Transform (DCT)
A new Modified Discrete Wavelets Packets Transform
(MDWPT) based method for the compression of Surface EMG signal (s-EMG) data is
presented. A Modified Discrete Wavelets Packets Transform (MDWPT) is applied to
the digitized s-EMG signal. A Discrete
Cosine Transforms (DCT) is applied to the MDWPT coefficients (only on detail
coefficients). The MDWPT+ DCT coefficients are quantized with a Uniform
Scalar Dead-Zone Quantizer (USDZQ).
An arithmetic coder is employed for the entropy coding of symbol streams. The proposed approach was tested on more than 35 actuals
S-EMG signals divided into three categories. The proposed approach was
evaluated by the following parameters:
Compression Factor (CF), Signal to Noise Ratio (SNR), Percent Root mean square Difference (PRD), Mean
Frequency Distortion (MFD) and the Mean Square Error (MSE). Simulation
results show that the proposed coding algorithm outperforms some recently
developed s-EMG compression algorithms.
References
[1]
Lu, Z., Kim, D.Y. and Pearlman, W.A. (2000) Wavelet Compression of ECG Signals by the Set Partitioning in Hierarchicaltreesalgorithm. IEEE Transactions on Biomedical Engineering, 47, 849-856. https://doi.org/10.1109/10.846678
[2]
Nygaard, R., Melnikov, G. and Katsaggelos, A.K. (2001) A Rate Distortion Optimal ECG Coding Algorithm. IEEE Transactions on Biomedical Engineering, 48, 28-40. https://doi.org/10.1109/10.900246
[3]
Blanco-Velasco, M., Cruz-Roldán, F., López-Ferreras, F., Bravo-Santos, A. and Martínez-Muñoz, D. (2004) A Low Computational Complexity Algorithm for ECG Signal Compression. Medical Engineering & Physics, 26, 553-568. https://doi.org/10.1016/j.medengphy.2004.04.004
[4]
Guerrero, A.P. and Mailhes, C. (1997) On the Choice of an Electromyogram Data Compression Method. Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, 30 October-2 November 1997, 1558-1561. https://doi.org/10.1109/IEMBS.1997.757009
[5]
Berger, P.A, Nascimento, F.A.O., Carmo, J.C., Rocha, A.F. and dos Santos, I. (2003) Algorithm for Compression of EMG Signals. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine Biology Society, 17-21 September 2003, Cancun, Mexico, 1299-1302. https://doi.org/10.1109/IEMBS.2003.1279508
[6]
Ntsama, E.P., Ele, P., Dieudonné, S.Z. and Tonye, E. (2013) Evaluation of EMG Signals Compression by JPEG 2000 Called 1D. International Journal of Engineering and Technology, 5, 44-51.
[7]
Ntsama, E.P., Ele, P., and Kabiena, I.B.V. (2013) Compression Approach of EMG Signal Using 2D Discrete Wavelet and Cosine Transforms. American Journal of Signal Processing, 3, 10-16.
[8]
Wellig, P., Cheng, Z., Semling, M. and Moschytz, G.S. (1998) Electromyogram Data Compression Using Single-Tree and Modified Zero-Tree Wavelet Encoding. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Hong Kong, China, 1 November 1998, 1303-1306. https://doi.org/10.1109/IEMBS.1998.747117
[9]
Chan, A. and Englehart, K.B. (2005) Continuous Myoelectric Control for Powered Protheses Using Hidden Markov Models. IEEE Transactions on Biomedical Engineer, 52, 121-124. https://doi.org/10.1109/TBME.2004.836492
[10]
Norris, J.A., Englehart, K.B. and Lovely, D.F. (2003) Myoelectric Signal Compression Using Zero-Trees of Wavelet Coefficients. Medical Engineering & Physics, 25, 739-746. https://doi.org/10.1016/S1350-4533(03)00118-8
[11]
Brechet, L., Lucas, M.F., Doncarli, C. and Farina, D. (2007) Compression of Biomedical Signals with Mother Wavelet Optimization and Best-Basis Wavelet Packet Selection. IEEE Transactions on Biomedical Engineering, 54, 2186-2192. https://doi.org/10.1109/TBME.2007.896596
[12]
Paiva, J.P.L.M., Kelencz, C.A., Paiva, H.M., Galvão, R.K.H. and Magini, M. (2008) Adaptive Wavelet EMG Compression Based on Local Optimization of Filter Banks. Physiological Measurement, 29, 843-856. https://doi.org/10.1088/0967-3334/29/7/012
[13]
Oyobé-Okassa, A.J. and Elé, P. (2014) Optimization of the Compression Ratio of the Modified Algorithm of Decomposition Electromyographic Signals by a Superimposed Coding. Proceedings of the 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, Marrakech, Morocco, 23-27 November 2014, 83-93. https://doi.org/10.1109/SITIS.2014.107
[14]
Sarkar, S. and Bhoi, A.K. (2012) Compression of Surface Electromyographic Signal Using Wavelet Packet 1D. International Journal of Pharmaceutical Sciences and Health Care, 4, 115-121.
[15]
Jain, N. and Vig, R. (2007) Wavelet Based Vector Quantization with Tree Code Vectors for EMG Signal Compression. Proceedings of the 6th WSEAS International Conference on Signal Process, Dallas, TX, 22-24 March 2007, 117-124.
[16]
Trabuco, M.H., Costa, M.V.C., Macchiavello, B. and de Oliveira Nascimento, F.A. (2018) S-EMG Signal Compression in One-Dimensional and Two-Dimensional Approaches. IEEE Journal of Biomedical and Health Informatics, 22, 1103-1113. https://doi.org/10.1109/JBHI.2017.2765922
[17]
Trabuco, M.H., Costa, M.V.C., Nascimento, F.A. and de Oliveira Nascimento, F.A. S-EMG Signal Compression Based on Domain Transformation and Spectral Shape Dynamic Bit Allocation. BioMedical Engineering OnLine, 13, Article No. 22. https://doi.org/10.1186/1475-925X-13-22
[18]
Berger, P.A., de Oliveira Nascimento, F.A., Rocha, A.F. and Carvalho, J.L.A. (2007) A New Wavelet-Based Algorithm for Compression of EMG Signals. Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, 22-26 August 2007, 1554-1557. https://doi.org/10.1109/IEMBS.2007.4352600
[19]
Costa, M.V.C., Berger, P.A., Rocha, A.F., de Carvalho, J.L.A. and de Oliveira Nascimento, F.A. (2008) Compression of Electromyographic Signals Using Image Compression Techniques. Proceedings of the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, 20-25 August 2008 , 2948-2951. https://doi.org/10.1109/IEMBS.2008.4649821
[20]
Costa, M.V.C., Carvalho, J.L.A., Berger, P.A., Zaghetto, A., Roch, A.F. and Nascimento, F.A.O. (2009) Two-Dimensional Compression of Surface Electromyographic Signals Using Column-Correlation Sorting and Image Encoders. Proceedings of the 2009Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, 3-6 September 2009, 428-431. https://doi.org/10.1109/IEMBS.2009.5333134
[21]
Melo, W.C., Filho, E.B.L. and Júnior, W.S.S. (2012) Electromyographic Signal Compression Based on Preprocessing Techniques. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, 28 August-1 September 2012 , 5404-5407. https://doi.org/10.1109/EMBC.2012.6347216
[22]
Melo, W.C., Filho, E.B.L. and Júnior, W.S.S. (2016) SEMG Signal Compression Based on Two Dimensional Techniques. BioMedical Engineering OnLine, 15, Article No. 41. https://doi.org/10.1186/s12938-016-0158-1
[23]
Ntsama, E.P., Lionel, T.M., Ele, P. and Kabiena, I.B. (2013) EMG Signal Compression Using 2D Fractal. International Journal of Advanced Technology and Engineering Research, 3, 58-68.
[24]
Itiki, C., Furuie, S.S. and Merletti, R. (2014) Compression of High-Density EMG Signals for Trapezius and Gastrocnemius Muscles. BioMedical Engineering OnLine, 13, Article No. 25. https://doi.org/10.1186/1475-925X-13-25
[25]
de Carvalho, M.B., da Silva, E.A.B. and Finamore, W.A. (2002) Multidimensional Signal Compression Using Multiscale Recurrent Patterns. Signal Process, 82, 1559-1580. https://doi.org/10.1016/S0165-1684(02)00302-X
[26]
Filho, E.B.L., Silva, E.A.B. and Carvalho, M.B. (2008) On EMG Signal Compression with Recurrent Patterns. IEEE Transactions on Biomedical Engineering, 55, 1920-1923. https://doi.org/10.1109/TBME.2008.919729
[27]
Salman, A., Allstot, E.G., Chen, A.Y., Dixon, A.M.R., Gangopadhyay, D. and Allstot D.J. (2011) Compressive Sampling of EMG Bio-Signals. Proceedings of the 2011 IEEE International Symposium of Circuits and Systems, Rio de Janeiro, 15-18 May 2011, 2095-2098. https://doi.org/10.1109/ISCAS.2011.5938011
[28]
Allstot, E.G., Chen, A.Y., Dixon, A.M.R., Gangopadhyay, D., Mitsuda, H. and Allstot, D.J. (2011) Compressed Sensing of ECG Bio-Signals Using One-Bit Measurement Matrices. Proceedings of the 2011 IEEE 9th International New Circuits and systems conference, Bordeaux, 26-29 June 2011, 213-216. https://doi.org/10.1109/NEWCAS.2011.5981293
[29]
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. https://doi.org/10.1109/TBCAS.2012.2193668
[30]
Chen, Y.-S., Lin, H.-Y., Chiu, H.-C. and Ma, H.-P. (2014) A Compressive Sensing Framework for Electromyogram and Electroencephalogram. 2014 IEEE International Symposium on Medical Measurements and Applications, Lisboa, Portugal, 11-12 June 2014, 1-6. https://doi.org/10.1109/MeMeA.2014.6860096
[31]
Welba, C., Ntsama, E.P. and Ele, P. (2016) Compression des Signaux Electro-myographiques par la Transformée en Paquets d’ondelettes Modifiée. Sciences, Technologies et Développement, Edition Spéciale, 17, 194-198. http://www.univ-douala.com/
[32]
Oyobe-Okassa, A.J., Abessolo, D.A. and Elé, P. (2016) Compression of EMG Signals by Superimposing Methods: Case of WPT and DCT. International Journal of Engineering and Technology, 8, 2319-8613.
[33]
Ahmed, N. and Rao, K.R. (1975) Orthogonal Transforms for Digital Signal Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45450-9
[34]
Cohen, A., Daubechies, I., Jawerth, B. and Vial, P. (1993) Multiresolution Analysis, Wavelets and Fast Algorithms on an Interval. Comptes Rendus de l’Académie des Sciences, Série I, 316, 417-421.
[35]
Goswami, J.C. and Chan, A.K. (1999) Fundamentals of Wavelets: Theory, Algorithms, and Applications. In: Wiley Series in Microwave and Optical Engineering, Wiley Interscience, Hoboken, NJ.
[36]
Mallat, S.G. (1989) A Theory for Multiresolution Signal Decomposition: The Wavelet Decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 674-693. https://doi.org/10.1109/34.192463
[37]
de Melo, W.C., de Lima Filho, E.B. and da Silva Júnior, W.S. (2016) Electromyographic Signal Compression through Image Encoders and Preprocessing Techniques. Journal of Communication and Information Systems, 31, 188-197. https://doi.org/10.14209/jcis.2016.17
[38]
Marcellin, W.M., Lepley, M.A., et al. (2002) An Overview of Quantization in JPEG 2000. Signal Processing: Image Communication, 17, 3-48. https://doi.org/10.1016/S0923-5965(01)00024-8
[39]
Medouakh, S. and Baarir, Z.-E. (2007) Compression d’images fixes par la norme jpeg2000. JIG’2007-3èmes Journées Internationales sur l’Informatique Graphique, LESIA Laboratoire de Recherche, Département Electronique, Université de Biskra, Alegria. 10-15.
[40]
Carotti, E.S.G., De Martin, J.C., Farina, D. and Merletti, R. (2005) Linear Predictive Coding of Myoelectric Signals. IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, PA, 23-23 March 2005, 629-632. https://doi.org/10.1109/ICASSP.2005.1416382
