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.

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

Marchioni, Alex;Mangia, Mauro;Pareschi, Fabio;Rovatti, Riccardo;Setti, Gianluca
2017

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.
2017
2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings
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Marchioni, Alex; Mangia, Mauro; Pareschi, Fabio; Rovatti, Riccardo; Setti, Gianluca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/656990
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