5.1 Conclusion
In this thesis, we first introduced the digital communication system. Then, we presented the detail derivation of a novel ISCD system with LBG, pseudo-Gray code, BCJR and SBSD algorithms. Based on the turbo-like decoding procedure, we proposed a initialization of a pri-ori knowledge. The parameter SNR performances of conventional and proposed ISCDs were simulated and compared. The PRD versus CR performance of proposed ISCD and other ECG compression techniques are analyzed.
Simulation results show conventional ISCD, the channel decoding effect under low channel SNR, the splitting method for index assignment, and the best parameters configuration. Com-pared to conventional ISCD, the proposed ISCD uses the extrinsic information of source index early at 0+thiteration. The parameter SNR performance enhances 3.76 dB at 0+thiteration with CR equals to 11. Additionally, convergence has been improved while applying the proposed ISCD. Additionally, the proposed ISCD does not require to train extra source statistics. Com-pared to other ECG compression techniques, the proposed ISCD achieves the highest CR/PRD ratio, low cost of time domain computation, simple source encoding, and error correctability.
We reduce complexity from transmitter part, and protect compressed ECG signal on a noisy channel.
5.2 Future Work
Future research directions of ISCD are listed as follows:
• Examine the ISCD performance under an MIMO, RF, or more realistic channel model.
• Examine the ISCD performance for different of interleaver and puncture tables
• Since the splitting method codebook construction introduces the most-significant-bit con-cept to vector quantizations. The unequal error protection of channel code should be considered.
• Pass the training sequence through the noisy channel to estimate the channel condition, in order to find the best parameters configuration of encoder.
Bibliography
[1] [Online]. Available: http://www.wica.intec.ugent.be/files/images/WBAN.img assist custom.jpg
[2] W. C. Mueller, “Arrhythmia detection program for an ambulatory ecg monitor,” Biomed.
Sci. Instrum., vol. 14, pp. 81–85, 1978.
[3] J. R. Cox, F. M. Nolle, H. A. Fozzard, and G. C. Oliver, “Aztec, a preprocessing program for real-time ecg rhythm analysis,” IEEE Transactions on Biomedical Engineering, vol.
BME-15, no. 2, pp. 128–129, 1968.
[4] J. P. Abenstein, “Algorithms for real-time ambulatory ecg monitoring,” Biomed. Sci. In-strum., vol. 14, pp. 73–79, 1978.
[5] R. Barr, S. M. Blanchard, and D. A. Dipersio, “Sapa-2 is the fan,” IEEE Transactions on Biomedical Engineering, vol. BME-32, no. 5, pp. 337–337, 1985.
[6] K. Rao and N. Ahmed, “Orthogonal transforms for digital signal processing,” in Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP ’76., vol. 1, 1976, pp. 136–140.
[7] Z. Lu, D. Y. Kim, and W. Pearlman, “Wavelet compression of ecg signals by the set par-titioning in hierarchical trees algorithm,” IEEE Transactions on Biomedical Engineering, vol. 47, no. 7, pp. 849–856, 2000.
[8] R. McCaughern, A. Rosie, and F. Monds, “Asynchronous data compression techniques,”
Proc. Purdue Centennial Year Symp. Information Process., vol. 2, pp. 525–531, 1969.
[9] A. Al-Shrouf, M. Abo-Zahhad, and M. S. Ahmed, “A novel compression algorithm for electrocardiogram signals based on the linear prediction of the wavelet coefficients,” Dig-ital signal. Process, vol. 13, pp. 604–622, 2003.
[10] A. Iwata, Y. Nagasaka, and N. Suzumura, “Data compression of the ecg using neural network for digital holter monitor,” IEEE Eng. Med. Bio1. Mag., vol. 9, no. 3, pp. 53–57, 1990.
[11] Z. Xiong, A. Liveris, and S. Cheng, “Distributed source coding for sensor networks,” IEEE Signal Processing Magazine, vol. 21, no. 5, pp. 80–94, 2004.
[12] S. Lloyd, “Least squares quantization in PCM,” IEEE Transactions on Information The-ory, vol. 28, no. 2, pp. 129–137, 1982.
[13] J. Max, “Quantizing for minimum distortion,” IRE Transactions on Information Theory, vol. 6, no. 1, pp. 7–12, 1960.
[14] Y. Linde, A. Buzo, and R. Gray, “An algorithm for vector quantizer design,” IEEE Trans-actions on Communications, vol. 28, no. 1, pp. 84–95, 1980.
[15] F. Gray, “Pulse code communication,” Patent US 2 632 058, 3 17, 1953. [Online].
Available: http://www.google.com/patents/US2632058
[16] K. Zeger and A. Gersho, “Pseudo-gray coding,” IEEE Transactions on Communications, vol. 38, no. 12, pp. 2147–2158, 1990.
[17] P. Elias, “Coding for noisy channels,” IRE Conv. Rec., vol. pt.4, pp. 37–47, 1955.
[18] C. Berrou, A. Glavieux, and P. Thitimajshima, “Near shannon limit error-correcting cod-ing and decodcod-ing: Turbo-codes. 1,” in IEEE International Conference on Communica-tions, 1993. ICC ’93 Geneva. Technical Program, Conference Record, vol. 2, 1993, pp.
1064–1070 vol.2.
[19] R. Gallager, “Low-density parity-check codes,” IRE Transactions on Information Theory, vol. 8, no. 1, pp. 21–28, 1962.
[20] J. Forney, G.D., “Codes on graphs: normal realizations,” IEEE Transactions on Informa-tion Theory, vol. 47, no. 2, pp. 520–548, 2001.
[21] J. L. Fan., Constrained coding and soft iterative decoding. Netherlands: Kluwer Aca-demic, 2001.
[22] R. Tanner, “A recursive approach to low complexity codes,” IEEE Transactions on Infor-mation Theory, vol. 27, no. 5, pp. 533–547, 1981.
[23] J. Hagenauer, E. Offer, and L. Papke, “Iterative decoding of binary block and convolutional codes,” IEEE Transactions on Information Theory, vol. 42, no. 2, pp. 429–445, 1996.
[24] A. Viterbi, “Error bounds for convolutional codes and an asymptotically optimum decod-ing algorithm,” IEEE Transactions on Information Theory, vol. 13, no. 2, pp. 260–269, 1967.
[25] J. M. Wozencraft and R. B., Sequential decoding. Cambridge: MIT Press and John Wiley, 1961.
[26] L. Bahl, J. Cocke, F. Jelinek, and J. Raviv, “Optimal decoding of linear codes for mini-mizing symbol error rate (corresp.),” IEEE Transactions on Information Theory, vol. 20, no. 2, pp. 284–287, 1974.
[27] J. Hagenauer and P. Hoeher, “A viterbi algorithm with soft-decision outputs and its appli-cations,” in IEEE Global Telecommunications Conference and Exhibition ’Communica-tions Technology for the 1990s and Beyond’ (GLOBECOM), 1989., 1989, pp. 1680–1686 vol.3.
[28] M. Adrat, P. Vary, and J. Spittka, “Iterative source-channel decoder using extrinsic infor-mation from softbit-source decoding,” in 2001 IEEE International Conference on Acous-tics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP ’01)., vol. 4, 2001, pp.
2653–2656 vol.4.
[29] J. Melsa and D. Cohn., Decision and Estimation Theory. Ltd.: McGraw-Hill Kogakusha, 1978.
[30] [Online]. Available:http://www.data-compression.com/vqanim.shtml.
[31] P. Robertson, E. Villebrun, and P. Hoeher, “A comparison of optimal and sub-optimal map decoding algorithms operating in the log domain,” in IEEE International Conference on Communications, 1995. ICC ’95 Seattle, ’Gateway to Globalization’, 1995, vol. 2, 1995, pp. 1009–1013 vol.2.
[32] J. Erfanian, S. Pasupathy, and P. Gulak, “Reduced complexity symbol detectors with par-allel structure for isi channels,” IEEE Transactions on Communications, vol. 42, no. 234, pp. 1661–1671, 1994.
[33] A. L. Goldberger, L. Amaral, L. Glass, J. M. Hausdorff, P. Ivanov, R. G. Mark, J. E. Mi-etus, G. B. Moody, C.-K. Peng, and H. E. Stanley. (2000, Jun.) Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals.
@ONLINE. [Online]. Available: http://circ.ahajournals.org/cgi/content/full/101/23/e215 [34] A. Graell i Amat, G. Montorsi, and S. Benedetto, “Design and decoding of optimal
high-rate convolutional codes,” IEEE Transactions on Information Theory, vol. 50, no. 5, pp.
867–881, 2004.
[35] S. Lee, J. Kim, and J.-H. Lee, “A retime ecg data compression and transmission al-gorithm for an e-health device,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 9, pp. 2448–2455, 2011.
[36] C.-T. Ku, K.-C. Hung, T.-C. Wu, and H.-S. Wang, “Wavelet-based ecg data compression system with linear quality control scheme,” IEEE Transactions on Biomedical Engineer-ing, vol. 57, no. 6, pp. 1399–1409, 2010.
[37] H. Mamaghanian, N. Khaled, D. Atienza, and P. Vandergheynst, “Compressed sensing for real-time energy-efficient ecg compression on wireless body sensor nodes,” IEEE Trans-actions on Biomedical EngineeringIEEE TransTrans-actions on, vol. 58, no. 9, pp. 2456–2466, 2011.