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, 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.2017.8325155].
Sparse sensing matrix based compressed sensing in low-power ECG sensor nodes
Pareschi F.;Setti G.
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.File | Dimensione | Formato | |
---|---|---|---|
08325155.pdf
accesso riservato
Descrizione: Editorial Version
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
219.15 kB
Formato
Adobe PDF
|
219.15 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
BioCAS2017-hw.pdf
accesso aperto
Descrizione: Author version of the Paper
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
Pubblico - Tutti i diritti riservati
Dimensione
570.83 kB
Formato
Adobe PDF
|
570.83 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2786599