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), pp. 11-17.
Titolo: | ECG compression retaining the best natural basis k-coefficients via sparse decomposition |
Autori: | ADAMO, ALESSANDRO (Primo) LANZAROTTI, RAFFAELLA (Penultimo) LIN, JIANYI (Ultimo) |
Parole Chiave: | ECG compression; Fixed-point iteration scheme; Orthogonal projections; Sparsity recovery |
Settore Scientifico Disciplinare: | Settore INF/01 - Informatica Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni |
Data di pubblicazione: | 2015 |
Rivista: | |
Tipologia: | Article (author) |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/j.bspc.2014.09.002 |
Appare nelle tipologie: | 01 - Articolo su periodico |
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