Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MeMeA49120.2020.9137307 | 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA) |
Keywords | DocType | ISBN |
Electrocardiogram,biomedical measurement system,Internet-of-Medical-Things (IoMT),multiple measurement vector reconstruction,Compressed Sensing,sub-sampling | Conference | 978-1-7281-5386-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Grazia Iadarola | 1 | 0 | 0.34 |
| Daponte, P. | 2 | 247 | 49.35 |
| Francesco Picariello | 3 | 0 | 1.35 |
| l de vito | 4 | 142 | 24.24 |