Low-complexity greedy algorithm in compressed sensing for the adapted decoding of ECGs

Alex Marchioni; Mauro Mangia; Fabio Pareschi; Riccardo Rovatti; Gianluca Setti

doi:10.1109/BIOCAS.2017.8325143

Low-complexity greedy algorithm in compressed sensing for the adapted decoding of ECGs

Alex Marchioni, Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

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.

Original language	English (US)
Title of host publication	2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Print)	9781509058037
DOIs	https://doi.org/10.1109/BIOCAS.2017.8325143
State	Published - Mar 23 2017
Externally published	Yes

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10.1109/BIOCAS.2017.8325143

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title = "Low-complexity greedy algorithm in compressed sensing for the adapted decoding of ECGs",

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.",

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Low-complexity greedy algorithm in compressed sensing for the adapted decoding of ECGs. / Marchioni, Alex; Mangia, Mauro; Pareschi, Fabio et al.
2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-4.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Low-complexity greedy algorithm in compressed sensing for the adapted decoding of ECGs

AU - Marchioni, Alex

AU - Mangia, Mauro

AU - Pareschi, Fabio

AU - Rovatti, Riccardo

AU - Setti, Gianluca

N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15

PY - 2017/3/23

Y1 - 2017/3/23

N2 - 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.

AB - 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.

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BT - 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Low-complexity greedy algorithm in compressed sensing for the adapted decoding of ECGs

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