Cardiovascular Signal Compression Using a New Wavelet Energy Based Diagnostic Distortion Measure

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This thesis presents an adaptive wavelet based compression method for cardiovascular signals. Generally, the performance of the lossy compression system depends on the methodologies used for compression and the quality measure used for evaluation of distortion. However, in order to introduce a closed loop rate or quality control, one needs an adequate distortion measure since it plays an important role in rate-distortion optimization technique used for finding optimal coding parameters. Generally, compression method with two-stage design employs global wavelet thresholding followed by fixed linear quantization approach. But this may introduce a severe signal distortion since a subband consists of wavelet coefficients with great magnitude differences and exhibits varying dynamic range according to the signal characteristics. Therefore, first an adaptive wavelet compression approach based on the preprocessing step, multiresolution signal decomposition (MSD) technique, classification of wavelet coefficients, constraint threshold control zero-zone nearly uniform midtread quantization (TCZNUMQ), modified index coding (MIC) and Huffman coding schemes is proposed in this work. Generally, the amplitude distribution of wavelet coefficients of most ECG signals has sharp concentrations around zero in their distributions, and the relevant wavelet coefficients of the signal contents appear very close in a sequence order within a wavelet subband. The proposed approach exploits the above properties using TCZNUMQ and MIC schemes for achieving substantial improvements in compression performance. In this approach, the wavelet coefficients are classified into frames based on the statistics of subband coefficients for providing better quantization. The classified coefficients are then quantized using the constraint TCZNUMQ scheme in an adaptive manner. In this quantizer design, the zero-zone width is defined by the threshold parameter T for wavelet thresholding and the outer-zone width is chosen according to the distortion specification. A constraint on the TCZNUMQ scheme is studied to further reduce the computational cost of the conventional two-stage scheme. Since indexes or location of the nonzero wavelet coefficients are in sequence order, the modified index coding scheme is used to compress the integer significance map by exploiting the redundancy among the indexes. The performance of compression method is tested using the well-known MIT-BIH arrhythmia (mita) database which contains varying characteristics of various ECG signals and different noises. The effect of noise filtering is one of the features in the wavelet transform based ECG signal compression. In such a case, smoothing of low-level background noise of the ECG signal causes a large percentage root mean square difference (PRD) value but no clinical feature distortion and, conversely, a small average distortion may severely deteriorate clinical performance if the error is concentrated in the regions of significant features. Moreover, noise present in the input decreases compression rate of the coder since the coder spends extra bits on approximating the noise for a user-specified PRD with a desired accuracy.
Supervisor: Samarendra Dandapat