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.
ECG compression retaining the best natural basis k-coefficients via sparse decomposition [pdf]Paper  ECG compression retaining the best natural basis k-coefficients via sparse decomposition [link]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.

Downloads: 6