The balanced weighted orthogonal matching pursuit (bWOMP) algorithm for recovering signals in compressed sensing (CS) based system is presented as a specialized recovering tool for Electrocardiograph (ECG) signals. Being based on the standard OMP approach, bWOMP is a lightweight reconstruction algorithm both in terms of complexity and memory footprint. Furthermore, the concept of weighting is introduced in the algorithm by exploring a prior knowledge on ECG signals. Experimental results show a performance increase of about 10 dB with respect to the standard OMP approach, and also an increase with respect to the decoding approaches considered as the state-of-the-art. In this case the gain could be as high as 4 dB with respect to the best of currently known decoding approaches.
Low-complexity greedy algorithm in compressed sensing for the adapted decoding of ECGs / Marchioni, A.; Mangia, M.; Pareschi, F.; Rovatti, R.; Setti, G.. - STAMPA. - 2018:(2017), pp. 1-4. (Intervento presentato al convegno 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 tenutosi a Torino (Italy) nel October 19-21, 2017) [10.1109/BIOCAS.2017.8325143].
Low-complexity greedy algorithm in compressed sensing for the adapted decoding of ECGs
Pareschi F.;Setti G.
2017
Abstract
The balanced weighted orthogonal matching pursuit (bWOMP) algorithm for recovering signals in compressed sensing (CS) based system is presented as a specialized recovering tool for Electrocardiograph (ECG) signals. Being based on the standard OMP approach, bWOMP is a lightweight reconstruction algorithm both in terms of complexity and memory footprint. Furthermore, the concept of weighting is introduced in the algorithm by exploring a prior knowledge on ECG signals. Experimental results show a performance increase of about 10 dB with respect to the standard OMP approach, and also an increase with respect to the decoding approaches considered as the state-of-the-art. In this case the gain could be as high as 4 dB with respect to the best of currently known decoding approaches.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2786597