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Deep Neural Oracles for Short-window Optimized Compressed Sensing of Biosignals.

Authors
  • Mangia, Mauro
  • Prono, Luciano
  • Marchioni, Alex
  • Pareschi, Fabio
  • Rovatti, Riccardo
  • Setti, Gianluca
Type
Published Article
Journal
IEEE Transactions on Biomedical Circuits and Systems
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Mar 23, 2020
Identifiers
DOI: 10.1109/TBCAS.2020.2982824
PMID: 32203026
Source
Medline
Language
English
License
Unknown

Abstract

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion. This architecture allows the definition of an encoding-decoding scheme with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG, thus allowing extremely low-complex encoders. As an additional feature, oracle-based recovery is able to self-assess, by indicating with remarkable accuracy chunks of signals that may have been reconstructed with a non-satisfactory quality. This self-assessment capability is unique in the CS literature and paves the way for further improvements depending on the requirements of the specific application. As an example, our scheme is able to satisfyingly compress by a factor of 2.67 an ECG or EEG signal with a complexity equivalent to only 24 signed sums per processed sample.

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