Abstract
Although digital storage media is not expensive and computational power has exponentially increased in past few years, the possibility of electrocardiogram (ECG) compression still attracts the attention, due to the huge amount of data that has to be stored and transmitted. ECG compression methods can be classified into two categories; direct method and transform method. A wide range of compression techniques were based on different transformation techniques. In this work, transform based signal compression is proposed. This method is used to exploit the redundancy in the signal. Wavelet based compression is evaluated to find an optimal compression strategy for ECG data compression. The algorithm for the one-dimensional case is modified and it is applied to compress ECG data. A wavelet ECG data code based on Run-length encoding compression algorithm is proposed in this research. Wavelet based compression algorithms for one-dimensional signals are presented along with the results of compression ECG data. Firstly, ECG signals are decomposed by discrete wavelet transform (DWT). The decomposed signals are compressed using thresholding and run-length encoding. Global and local thresholding are employed in the research. Different types of wavelets such as daubechies, haar, coiflets and symlets are applied for decomposition. Finally the compressed signal is reconstructed. Different types of wavelets are applied and their performances are evaluated in terms of compression ratio (CR), percent root mean square difference (PRD). Compression using HAAR wavelet and local thresholding are found to be optimal in terms of compression ratio.
Highlights
Compression/coding of Electrocardiogram (ECG) signal are done by detecting and removing redundant information from the ECG signal [1]
All the results presented in this study are based on run-length encoding after wavelet transform
MATLAB program is applied on a set of ECG signals in order to investigate the performance of the proposed compression technique
Summary
Compression/coding of Electrocardiogram (ECG) signal are done by detecting and removing redundant information from the ECG signal [1]. One method is direct data compression method [2], which detects redundancies by direct analysis of actual signal samples. Another method is transform method, which first transforms the signal to some other time–frequency representations better suited for detecting and removing redundancies. The main goal of an optimized compression technique is to minimize the number of samples needed to transmit the ECG without losing the remarkable information of the original signal in order to achieve a correct clinical diagnosis
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