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.
ECG compression retaining the best natural basis k-coefficients via sparse decomposition / A. Adamo, G. Grossi, R. Lanzarotti, J. Lin. - In: BIOMEDICAL SIGNAL PROCESSING AND CONTROL. - ISSN 1746-8094. - 15(2015 Jan), pp. 11-17. [10.1016/j.bspc.2014.09.002]
ECG compression retaining the best natural basis k-coefficients via sparse decomposition
A. AdamoPrimo
;G. Grossi
;R. LanzarottiPenultimo
;J. LinUltimo
2015
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.File | Dimensione | Formato | |
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