ECG compression retaining the best natural basis k-coefficients via sparse decomposition. Adamo, A., Grossi, G., Lanzarotti, R., & Lin, J. Biomedical Signal Processing and Control, 15:11-17, Elsevier Ltd, 2015.
Paper
Website doi abstract bibtex 6 downloads A novel and efficient signal compression algorithm aimed at finding the sparsest representation of electro-cardiogram (ECG) signals is presented and analyzed. The idea behind the method relies on basis elementsdrawn from the initial transitory of a signal itself, and the sparsity promotion process applied to its sub-sequent blocks grabbed by a sliding window. The saved coefficients rescaled in a convenient range, quantized and compressed by a lossless entropy-based algorithm. Experiments on signals extracted from the MIT-BIH Arrhythmia database show that the methodachieves in most of the cases very high performance.
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abstract = {A novel and efficient signal compression algorithm aimed at finding the sparsest representation of electro-cardiogram (ECG) signals is presented and analyzed. The idea behind the method relies on basis elementsdrawn from the initial transitory of a signal itself, and the sparsity promotion process applied to its sub-sequent blocks grabbed by a sliding window. The saved coefficients rescaled in a convenient range, quantized and compressed by a lossless entropy-based algorithm. Experiments on signals extracted from the MIT-BIH Arrhythmia database show that the methodachieves in most of the cases very high performance.},
bibtype = {article},
author = {Adamo, Alessandro and Grossi, Giuliano and Lanzarotti, Raffaella and Lin, Jianyi},
doi = {10.1016/j.bspc.2014.09.002},
journal = {Biomedical Signal Processing and Control}
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Downloads: 6
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