Sparse sensing matrix based compressed sensing in low-power ECG sensor nodes

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

doi:10.1109/BIOCAS.2017.8325155

Sparse sensing matrix based compressed sensing in low-power ECG sensor nodes

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

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

5 Scopus citations

Abstract

Compressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., energy requirements) of a signal processing system thanks to its capability of simultaneously sampling and compressing an input waveform. Here we focus on Electrocardiogram (ECG) signals acquired by means of a custom designed acquisition board that exploits CS as early-digital compression stage. We show that when CS acquisition sequences are sparse ternary, i.e., with symbols {-1, 0, +1} and designed to maximize their rakeness, it is possible to achieve a reduction in the energy required for ECG signal compression by a factor between 25 and 30 with respect to the standard acquisition with independent and identically distributed random sequences.

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.2017.8325155
State	Published - Mar 23 2017
Externally published	Yes

Bibliographical note

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

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10.1109/BIOCAS.2017.8325155

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abstract = "Compressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., energy requirements) of a signal processing system thanks to its capability of simultaneously sampling and compressing an input waveform. Here we focus on Electrocardiogram (ECG) signals acquired by means of a custom designed acquisition board that exploits CS as early-digital compression stage. We show that when CS acquisition sequences are sparse ternary, i.e., with symbols {-1, 0, +1} and designed to maximize their rakeness, it is possible to achieve a reduction in the energy required for ECG signal compression by a factor between 25 and 30 with respect to the standard acquisition with independent and identically distributed random sequences.",

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Sparse sensing matrix based compressed sensing in low-power ECG sensor nodes. / 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 - Sparse sensing matrix based compressed sensing in low-power ECG sensor nodes

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 - Compressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., energy requirements) of a signal processing system thanks to its capability of simultaneously sampling and compressing an input waveform. Here we focus on Electrocardiogram (ECG) signals acquired by means of a custom designed acquisition board that exploits CS as early-digital compression stage. We show that when CS acquisition sequences are sparse ternary, i.e., with symbols {-1, 0, +1} and designed to maximize their rakeness, it is possible to achieve a reduction in the energy required for ECG signal compression by a factor between 25 and 30 with respect to the standard acquisition with independent and identically distributed random sequences.

AB - Compressed Sensing (CS) is an acquisition technique able to reduce the operating cost (e.g., energy requirements) of a signal processing system thanks to its capability of simultaneously sampling and compressing an input waveform. Here we focus on Electrocardiogram (ECG) signals acquired by means of a custom designed acquisition board that exploits CS as early-digital compression stage. We show that when CS acquisition sequences are sparse ternary, i.e., with symbols {-1, 0, +1} and designed to maximize their rakeness, it is possible to achieve a reduction in the energy required for ECG signal compression by a factor between 25 and 30 with respect to the standard acquisition with independent and identically distributed random sequences.

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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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Sparse sensing matrix based compressed sensing in low-power ECG sensor nodes

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