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.
Marchioni, A., Mangia, M., Pareschi, F., Rovatti, R., Setti, G. (2017). Sparse sensing matrix based compressed sensing in low-power ECG sensor nodes. Institute of Electrical and Electronics Engineers Inc. [10.1109/BIOCAS.2017.8325155].
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
