Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals

Mauro Mangia, Luciano Prono, Alex Marchioni, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)545-557
Number of pages13
JournalIEEE Transactions on Biomedical Circuits and Systems
Issue number3
StatePublished - Jun 1 2020
Externally publishedYes

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


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