Journal of medical engineering & technology
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This paper presents a modified version of Set Partitioning In Hierarchical Trees (SPIHT) wavelet compression method, which has been developed for ECG signal compression. Two more steps in the existing technique have been added to achieve higher compression ratio (CR) and lower percentage rms difference (PRD). The method has been tested on selected records from the MIT-BIH arrhythmia database. Even with two more steps, the method retains its simplicity, computational efficiency and self-adaptiveness, without compromising on any other performance parameter.
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Increasing use of computerized ECG processing systems requires effective electrocardiogram (ECG) data compression techniques which aim to enlarge storage capacity and improve data transmission over phone and internet lines. This paper presents a compression technique for ECG signals using the singular value decomposition (SVD) combined with discrete wavelet transform (DWT). The central idea is to transform the ECG signal to a rectangular matrix, compute the SVD, and then discard small singular values of the matrix. ⋯ The technique has been tested on ECG signals obtained from MIT-BIH arrhythmia database. The results showed that data reduction with high signal fidelity can thus be achieved with average data compression ratio of 25.2:1 and average PRD of 3.14. Comparison between the obtained results and recently published results show that the proposed technique gives better performance.