This paper proposes a dynamic method based on Compressed Sensing (CS) to reconstruct multi-lead electrocardiography (ECG) signals in support of Internet-of-Medical-Things. Specifically, the sensing matrix is dynamically evaluated through the signal samples acquired by the first lead. The experimental evaluation demonstrates that, compared to the traditional CS multi-lead method adopting a random sensing matrix, the proposed dynamic method exhibits a lower difference from the original ECG signal.

A Dynamic Approach for Compressed Sensing of Multi-lead ECG Signals / Iadarola, G.; Daponte, P.; Picariello, F.; De Vito, L.. - (2020), pp. 1-6. (Intervento presentato al convegno 15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020 tenutosi a ita nel 2020) [10.1109/MeMeA49120.2020.9137307].

A Dynamic Approach for Compressed Sensing of Multi-lead ECG Signals

Iadarola G.
Primo
;
Daponte P.;
2020-01-01

Abstract

This paper proposes a dynamic method based on Compressed Sensing (CS) to reconstruct multi-lead electrocardiography (ECG) signals in support of Internet-of-Medical-Things. Specifically, the sensing matrix is dynamically evaluated through the signal samples acquired by the first lead. The experimental evaluation demonstrates that, compared to the traditional CS multi-lead method adopting a random sensing matrix, the proposed dynamic method exhibits a lower difference from the original ECG signal.
2020
978-1-7281-5386-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/311032
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