Low-power ECG acquisition by Compressed Sensing with Deep Neural Oracles

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

The recovery of sparse signals from their linear mapping on a lower-dimensional space is traditionally performed by finding the sparsest solution compatible with such solutions. This task can be partitioned in two phases: support estimation and coefficient estimation. We propose to perform the former with a deep neural network jointly trained with the encoder that divines a support that is used in the latter phase to estimate the coefficients by pseudo-inversion. Numerical evidence demonstrates that the proposed encoder-decoder architecture outperforms state-of-the-art Compressed Sensing (CS) approaches in the recovery of synthetic ECG signals for a compression ratio higher than 2.5. Further tests on real ECG prove the applicability in real-world scenarios.
Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages158-162
Number of pages5
ISBN (Print)9781728149226
DOIs
StatePublished - Aug 1 2020
Externally publishedYes

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