@article{
 title = {ECG compression retaining the best natural basis k-coefficients via sparse decomposition},
 type = {article},
 year = {2015},
 keywords = {ECG compression,Fixed-point iteration scheme,Orthogonal projections,Sparsity recovery},
 pages = {11-17},
 volume = {15},
 websites = {http://dx.doi.org/10.1016/j.bspc.2014.09.002},
 publisher = {Elsevier Ltd},
 id = {ed29557c-cc40-32df-989f-5d53f5d16dcd},
 created = {2018-04-23T03:55:47.914Z},
 file_attached = {true},
 profile_id = {6bce6ab9-03b5-36ad-a474-26e482dc52c3},
 last_modified = {2018-04-23T03:56:29.709Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 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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