Energy Analysis of Decoders for Rakeness-Based Compressed Sensing of ECG Signals

Fabio Pareschi, Mauro Mangia, Daniele Bortolotti, Andrea Bartolini, Luca Benini, Riccardo Rovatti, Gianluca Setti

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


In recent years, compressed sensing (CS) has proved to be effective in lowering the power consumption of sensing nodes in biomedical signal processing devices. This is due to the fact the CS is capable of reducing the amount of data to be transmitted to ensure correct reconstruction of the acquired waveforms. Rakeness-based CS has been introduced to further reduce the amount of transmitted data by exploiting the uneven distribution to the sensed signal energy. Yet, so far no thorough analysis exists on the impact of its adoption on CS decoder performance. The latter point is of great importance, since body-area sensor network architectures may include intermediate gateway nodes that receive and reconstruct signals to provide local services before relaying data to a remote server. In this paper, we fill this gap by showing that rakeness-based design also improves reconstruction performance. We quantify these findings in the case of ECG signals and when a variety of reconstruction algorithms are used either in a low-power microcontroller or a heterogeneous mobile computing platform.
Original languageEnglish (US)
Pages (from-to)1278-1289
Number of pages12
JournalIEEE Transactions on Biomedical Circuits and Systems
Issue number6
StatePublished - Dec 1 2017
Externally publishedYes

Bibliographical note

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

ASJC Scopus subject areas

  • Biomedical Engineering
  • Electrical and Electronic Engineering


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