Showing papers by "Sabah M. Ahmed published in 2008"

Optimal selection of threshold levels and wavelet filters for high quality ecg signal compression

Abstract: Although most of the theoretical and implementation aspects of wavelet based algorithms in ElectroCardioGram (ECG) signal compression are well studied, many issues related to the choice of wavelet filters and threshold levels selection remain unresolved. The utilization of optimal mother wavelet will lead to localization and maximization of wavelet coefficients' values in wavelet domain. This paper presents an ECG compressor based on the optimal selection of wavelet filters and threshold levels in different subbands that achieve maximum data volume reduction while guaranteeing reconstruction quality. The proposed algorithm starts by segmenting the ECG signal into frames; where each frame is decomposed into m subbands through optimized wavelet filters. The resulting wavelet coefficients are threshold and those having absolute values below specified threshold levels in all subands are deleted and the remaining coefficients are appropriately encoded with a modified version of the run-length coding scheme. The threshold levels to use, before encoding, are adjusted in an optimum manner, until predefined compression ratio and signal quality are achieved. Extensive experimental tests were made by applying the algorithm to ECG records from the MIT-BIH Arrhythmia Database [1]. The compression ratio (CR), the percent root-mean-square difference (PRD) and the zero-mean percent root-mean-square difference (PRD1) measures are used for measuring the algorithm performance (high CR with excellent reconstruction quality). From the obtained results, it can be deduced that the performance of the optimized signal dependent wavelet outperforms that of Daubechies and Coiflet standard wavelets. However, the computational complexity of the proposed technique is the price paid for the improvement in the compression performance measures. Finally, it should be noted that the proposed method is flexible in controlling the quality of the reconstructed signals and the volume of the compressed signals by establishing target PRD and CR a priori respectively.

An ECG signal compressor based on the selection of optimal threshold levels of discrete wavelet transform coefficients.

Abstract: Compression of electrocardiography (ECG) is necessary for efficient storage and transmission of the digitized ECG signals. Discrete wavelet transform (DWT) has recently emerged as a powerful technique for ECG signal compression due to its multi-resolution signal decomposition and locality properties. This paper presents an ECG compressor based on the selection of optimum threshold levels of DWT coefficients in different subbands that achieve maximum data volume reduction while preserving the significant signal morphology features upon reconstruction. First, the ECG is wavelet transformed into m subbands and the wavelet coefficients of each subband are thresholded using an optimal threshold level. Thresholding removes excessively small features and replaces them with zeroes. The threshold levels are defined for each signal so that the bit rate is minimized for a target distortion or, alternatively, the distortion is minimized for a target compression ratio. After thresholding, the resulting significant wavelet coefficients are coded using multi embedded zero tree (MEZW) coding technique. In order to assess the performance of the proposed compressor, records from the MIT-BIH Arrhythmia Database were compressed at different distortion levels, measured by the percentage rms difference (PRD), and compression ratios (CR). The method achieves good CR values with excellent reconstruction quality that compares favourably with various classical and state-of-the-art ECG compressors. Finally, it should be noted that the proposed method is flexible in controlling the quality of the reconstructed signals and the volume of the compressed signals by establishing a target PRD and a target CR a priori, respectively.

Design of iir filters having specified magnitude and group delay characteristics using evolutionary immune algorithm

Abstract: This paper presents a new method based on antigen-antibody reaction of immune system in biology, has been adopted for the design of IIR digital filter using an evolutionary Tagushi Immune Algorithm (TIA). The design process is reduced to a constrained optimization problem the solution of which is achieved by the convergence of the TIA which implements a learning technique inspired by human immune system. The main feature of the immune algorithm is its ability to find global optimal solution in a nonlinear search space with stability guaranteed. In the design process, all the filter coefficients are collected as antibodies, and a population of these antibodies is evolved by using immune algorithm operations of reproduction, and clonal proliferation within hyper-mutation. Taguashi quality control technique has been adopted for a better selection of antibodies to generate more excellent offsprings. It is applied to the design of IIR digital filters exhibiting either specified time-domain or specified magnitude and group delay characteristics. Illustrative examples are presented to verify the effectiveness of the proposed method compared to the existing hierarchical genetic [22]-[23], and immune based algorithm [24].