@inProceedings{
 title = {Gaussian dictionary for Compressive Sensing of the ECG signal},
 type = {inProceedings},
 year = {2014},
 identifiers = {[object Object]},
 id = {5b165979-ade8-36a4-8025-a402ef7fd1a2},
 created = {2017-07-10T23:23:40.179Z},
 file_attached = {false},
 profile_id = {e9074be7-9115-38b3-bbbd-5682317d4502},
 last_modified = {2017-07-10T23:23:40.179Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2014 IEEE.Compressive Sensing (CS) is a newly introduced signal processing technique that enables to recover sparse signals from fewer samples than the Shannon sampling theorem would typically require. It is based on the assumption that, for a sparse signal, a small collection of linear measurements contains enough information to allow its reconstruction. Combining the acquisition and compression stages, CS is a very promising technique to develop ultra low power wireless bio-signal monitoring systems. In this paper we present a Compressive Sensing framework for ECG signals based on a universal Gaussian over-complete dictionary that permits to successfully increase the reconstruction quality performance. The purpose of the proposed dictionary is to improve ECG signal sparsity in order to achieve a higher compression ratio. Numerical experiments demonstrate that our method achieves improved performance with respect to state-of-the-art CS schemes.},
 bibtype = {inProceedings},
 author = {Da Poian, G. and Bernardini, R. and Rinaldo, R.},
 booktitle = {BIOMS 2014 - 2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, Proceedings}
}

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