Adaptive Data Compression in Wireless Body Sensor Networks

This paper introduces an innovative data compression methodology in an adaptive way guaranteeing signal interpretation quality and energy efficiency in wireless body area sensor network. The approach is based on the discrete cosine transform. Under different channel conditions, the resulting wavelet coefficients are thresholded adaptively to match a user-specified percentage of root-mean-square difference. The nonzero coefficients of the thresholded vector are quantized adaptively by the linear quantizer of the lowest possible resolution. Signal interpretation quality and energy consuming are influenced by threshold Th, quantization length Q, and modulation method M which can be taken as a discrete optimization problem. A specific case study is designed to achieve better energy efficiency and guaranteed signal interpretation quality than typical efforts. The simulation results show that the proposed compression scheme can achieve considerable gains for ECG signals in wireless body area sensor networks.

[1]  K. R. Rao,et al.  Orthogonal Transforms for Digital Signal Processing , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Amjed S. Al-Fahoum,et al.  Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure , 2006, IEEE Transactions on Information Technology in Biomedicine.

[3]  A. Cohen,et al.  ECG compression using long-term prediction , 1993, IEEE Transactions on Biomedical Engineering.

[4]  W. A. Coberly,et al.  ECG data compression techniques-a unified approach , 1990, IEEE Transactions on Biomedical Engineering.

[5]  John G. Proakis,et al.  Digital Communications , 1983 .

[6]  M.L. Hilton,et al.  Wavelet and wavelet packet compression of electrocardiograms , 1997, IEEE Transactions on Biomedical Engineering.

[7]  Álvaro Alesanco Iglesias,et al.  A Simple Method for Guaranteeing ECG Quality in Real-Time Wavelet Lossy Coding , 2007, EURASIP J. Adv. Signal Process..

[8]  Ii Leon W. Couch.  Digital and analog communication systems , 1983 .

[9]  I. S. N. Murthy,et al.  ECG Data Compression Using Fourier Descriptors , 1986, IEEE Transactions on Biomedical Engineering.

[10]  Hamid Sharif,et al.  Taming Underlying Design for Energy Efficient Distributed Source Coding in Multirate Wireless Sensor Network , 2007, 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring.

[11]  Hamid Sharif,et al.  Energy-Constrained Distortion Reduction Optimization for Wavelet-Based Coded Image Transmission in Wireless Sensor Networks , 2008, IEEE Transactions on Multimedia.

[12]  Hamid Sharif,et al.  Optimal Image Component Transmissions in Multirate Wireless Sensor Networks , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[13]  William A. Pearlman,et al.  Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm , 2000, IEEE Transactions on Biomedical Engineering.

[14]  Hamid Sharif,et al.  Energy Efficient Multirate Interaction in Distributed Source Coding and Wireless Sensor Network , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[15]  P. Laguna,et al.  Adaptive estimation of QRS complex wave features of ECG signal by the hermite model , 2007, Medical and Biological Engineering and Computing.

[16]  Hamid Sharif,et al.  Interplay Between Routing and Distributed Source Coding in Wireless Sensor Network , 2007, 2007 IEEE International Conference on Communications.

[17]  W.J. Tompkins,et al.  Compression of the ambulatory ECG by average beat subtraction and residual differencing , 1991, IEEE Transactions on Biomedical Engineering.

[18]  G.D. Barlas,et al.  A novel family of compression algorithms for ECG and other semiperiodical, one-dimensional, biomedical signals , 1996, IEEE Transactions on Biomedical Engineering.

[19]  B. P. Lathi,et al.  Modern Digital and Analog Communication Systems , 1983 .

[20]  Redha Benzid,et al.  Electrocardiogram Compression Method Based on the Adaptive Wavelet Coefficients Quantization Combined to a Modified Two-Role Encoder , 2007, IEEE Signal Processing Letters.