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EnglishThe 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.
Low-power ECG acquisition by Compressed Sensing with Deep Neural Oracles / Mangia, M.; Marchioni, A.; Prono, L.; Pareschi, F.; Rovatti, R.; Setti, G.. - STAMPA. - (2020), pp. 158-162. ((Intervento presentato al convegno 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020 tenutosi a virtuale nel AUGUST 31ST - SEPTEMBER 4TH 2020.
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| Titolo: | Low-power ECG acquisition by Compressed Sensing with Deep Neural Oracles | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Abstract: | The recovery of sparse signals from their linear mapping on a lower-dimensional space is traditio...nally 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. | |
| ISBN: | 978-1-7281-4922-6 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| AICAS2020.pdf | Author version of the Paper | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
| 09073945.pdf | Editorial Version | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2846086
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