S-EMG signal compression based on domain transformation and spectral shape dynamic bit allocation
Tóm tắt
Surface electromyographic (S-EMG) signal processing has been emerging in the past few years due to its non-invasive assessment of muscle function and structure and because of the fast growing rate of digital technology which brings about new solutions and applications. Factors such as sampling rate, quantization word length, number of channels and experiment duration can lead to a potentially large volume of data. Efficient transmission and/or storage of S-EMG signals are actually a research issue. That is the aim of this work. This paper presents an algorithm for the data compression of surface electromyographic (S-EMG) signals recorded during isometric contractions protocol and during dynamic experimental protocols such as the cycling activity. The proposed algorithm is based on discrete wavelet transform to proceed spectral decomposition and de-correlation, on a dynamic bit allocation procedure to code the wavelets transformed coefficients, and on an entropy coding to minimize the remaining redundancy and to pack all data. The bit allocation scheme is based on mathematical decreasing spectral shape models, which indicates a shorter digital word length to code high frequency wavelets transformed coefficients. Four bit allocation spectral shape methods were implemented and compared: decreasing exponential spectral shape, decreasing linear spectral shape, decreasing square-root spectral shape and rotated hyperbolic tangent spectral shape. The proposed method is demonstrated and evaluated for an isometric protocol and for a dynamic protocol using a real S-EMG signal data bank. Objective performance evaluations metrics are presented. In addition, comparisons with other encoders proposed in scientific literature are shown. The decreasing bit allocation shape applied to the quantized wavelet coefficients combined with arithmetic coding results is an efficient procedure. The performance comparisons of the proposed S-EMG data compression algorithm with the established techniques found in scientific literature have shown promising results.
Tài liệu tham khảo
Carotti ESG, De Martin JC, Farina D, Merletti R: Linear predictive coding of myoelectric signals. In Proceedings of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing: 18–23 March 2005. Vol 5. Philadelphia; 2005:629–632.
Guerrero AP, Mailhes C: On the choice of an electromyogram data compression method. In Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 30 October - 2 November 1997. Vol. 6. Chicago; 1997:1558–1561.
Berger PA, Nascimento FAO, Carmo JC, Rocha AF, Dos Santos I: Algorithm for compression of EMG signals. In Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine Biology Society: 17–21 September 2003. Vol. 2. Cancun; 2003:1299–1302.
Carotti ESG, De Martin JC, Merletti R, Farina D: Compression of surface EMG signals with algebraic code excited linear prediction. Med Eng Phys 2007,29(2):253–258. 10.1016/j.medengphy.2006.03.004
Norris JF, Lovely DF: Real-time compression of myoelectric data utilizing adaptive differential pulse code modulation. Med Biol Eng Comput 1995,33(5):629–635. 10.1007/BF02510779
Wellig P, Zhenlan C, Semling M, Moschytz GS: Electromyogram data compression using single-tree and modified zero-tree wavelet encoding. In Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 29 October - 1 November 1998. Vol. 3. Hong Kong; 1998:1303–1306.
Norris JA, Englehart K, Lovely D: Steady-state and dynamic myoelectric signal compression using embedded zero-tree wavelets. In Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine Biology Society: 25–28 October 2001. Vol. 2. Istanbul; 2001:1879–1882.
Brechet L, Lucas MF, Doncarli C, Farina D: Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection. IEEE Trans Biomed Eng 2007,54(12):2186–2192.
Paiva JPLM, Kelencz CA, Paiva HM, Galvão RKH, Magini M: Adaptive wavelet EMG compression based on local optimization of filter banks. Physiol Meas 2008,29(7):843–856. ISSN 1361–6597 10.1088/0967-3334/29/7/012
Gronfors TK, Paivinen NS: Comparison of vector quantization methods for medical fidelity preserving lossy compression of EMG signals. In Int Conf Comput Intell Model, Control Automation: 28–30 November 2005. Vienna; 2005:1107–1113.
Grönfors T, Reinikainen M, Sihvonen T: Vector quantization as a method for integer EMG signal compression. J Med Eng Technol 2006,30(1):41–52. 10.1080/03091900500130872
Jain N, Vig R: Wavelet based vector quantization with tree code vectors for EMG Signal compression. In Proceedings of the 6th WSEAS International Conference on Signal Processing: 22–24 March 2007. Vol. 6. Dallas; 2007:117–124.
Berger PA, Nascimento FAO, Carmo JC, Rocha AF: Compression of EMG signals with wavelet transform and artificial neural networks. Physiol Meas England 2006,27(6):457–465. 10.1088/0967-3334/27/6/003
Berger PA, Nascimento FAO, Rocha AF, Carvalho JLA: A new wavelet-based algorithm for compression of EMG signals. In Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 22–26 August 2007. Lyon; 2007:1554–1557.
Filho EBL, Silva EAB, Carvalho MB: On EMG signal compression with recurrent patterns. IEEE Trans Biomed Eng 2008,55(7):1920–1923. ISSN 0018–9294
Carotti ESG, De Martin JC, Merletti R, Farina D: ACELP-based compression of multi-channel surface EMG signals. In Proceedings of the 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing: 15–20 April 2007. Vol. 2. Honolulu; 2007:II-361-II-364.
Carotti ESG, De Martin JC, Merletti R, Farina D: Matrix-based linear predictive compression of multi-channel surface EMG signals. In Proceedings of the 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing: 31 March - 4 April 2008. Las Vegas; 2008:493–496.
Carotti ESG, De Martin JC, Merletti R, Farina D: Compression of multidimensional biomedical signals with spatial and temporal codebook-excited linear prediction. IEEE Trans Biomed Eng 2009,56(11):2604–2610.
Costa MVC, Berger PA, Rocha AF, Carvalho JL, Nascimento FAO: Compression of Electromyographic Signals Using Image Compression Techniques. In Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 20–25 August 2008. Vancouver; 2008:2948–2951.
Costa MVC, Carvalho JLA, Berger PA, Zaghetto A, Rocha AF, Nascimento FAO: Two-dimensional compression of surface electromyographic signals using column-correlation sorting and image encoders. In Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 3–6 September 2009. Minneapolis; 2009:428–431.
Costa MVC, Carvalho JLA, Berger PA, Rocha AF, Nascimento FAO: Compression of surface electromyographic signals using two-dimensional techniques. In Recent Advances in Biomedical Engineering. Edited by: Naik GR. Rijeka, Croatia: InTech; 2009:17–38.
Ntsama EP, Ele P, Kabiena IB: Compression approach of EMG signal using 2D discrete wavelet and cosine transforms. Am J Signal Process 2013,3(1):10–16.
Ntsama EP, Mbaidoun L, Ele P, Kabiena IB: EMG signal compression using 2D fractal. Int J Adv Technol Eng Res(IJATER) 2013,3(3):58–89.
Ntsama EP, Pierre ESZD, Serfebé ZD, Emmanuel T: Evaluation of EMG signals compression by JPEG 2000 called 1D. Int J Eng Technol(IJET) 2013,5(1):44–51.
Salman A, Allstot EG, Chen AY, Dixon AMR, Gangopadhyay D, Allstot DJ: Compressive sampling of EMG bio-signals. In Proceedings of the 2011 IEEE International Symposium on Circuits and Systems: 15–18 May 2011. Rio de Janeiro; 2011:2095–2098.
Dixon AMR, Allstot EG, Gangopadhyay D, Allstot DJ: Compressed sensing system considerations for ECG and EMG wireless biosensors. IEEE Trans Biomed Circuits Syst 2012,6(2):156–166.
Witten IH, Neal RM, Cleary JG: Arithmetic coding for data compression. Commun ACM 1987,30(6):520–540. 10.1145/214762.214771