24-09-2012, 11:32 AM
Efficient Coding Algorithm for the Compression of ECG Signals Using Wave let transforms.
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ABSTRACT
Considering that the number of electrocardiogram records annually numbers in the millions and the use of sending electrocardiogram records over telephone lines for remote analysis is increasing, the need for effective electrocardiogram compression techniques is great. Many existing compression algorithms have shown some success in electrocardiogram compression; however, algorithms that produce better compression ratios and less loss of data in the reconstructed data are needed. This research will provide an overview of several compression techniques and will formulate new algorithms that should improve compression ratios and lessen error in the reconstructed data.
Wavelet transform analysis has emerged as a major new time-frequency decomposition tool for data analysis. The wavelet transform has been found to be particularly useful for analyzing signals which are transitory, discontinuous, noisy, and so on. Its ability to examine the signal in both time and frequency resolution is distinctive and enables myriads of applications possible that traditional signal analysis tools such as Fourier transform cannot handle. It has now been applied to diverse realm of data analysis/process: climate analysis, financial indices analysis, signal denoising, characterization, feature extraction, data compression, and so on.
A wavelet-based electrocardiogram (ECG) data compression algorithm is proposed in this project. TheECGsignal is first preprocessed, the discrete wavelet transform (DWT) is then applied to the preprocessed signal. Preprocessing guarantees that the magnitudes of the wavelet coefficients be less than one, and reduces the reconstruction errors near both ends of the compressed signal. The DWT coefficients are divided into three groups, each group is thresholded using a threshold based on a desired energy packing efficiency. A binary significance map is then generated by scanning the wavelet decomposition coefficients and outputting a binary one if the scanned coefficient is significant, and a binary zero if it is insignificant. Compression is achieved by run length encoding to compress the significance map. The ability of the coding algorithm to compressECGsignals is investigated, the results were obtained by compressing and decompressing the test signals. The proposed algorithm is compared with DCT-based and wavelet-based compression algorithms and showed superior performance.