Name
Abstract | ||
|---|---|---|
This paper reviews recent advances in non-invasive blood pressure monitoring and highlights the added value of a novel algorithm-based blood pressure sensor which uses machine-learning techniques to extract blood pressure values from the shape of the pulse waveform. We report results from preliminary studies on a range of patient populations and discuss the accuracy and limitations of this capacitive-based technology and its potential application in hospitals and communities. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21134273 | SENSORS |
Keywords | DocType | Volume |
cNIBP, neonate, NICU, hypertension, hypotension, non-invasive blood pressure monitoring | Journal | 21 |
Issue | ISSN | Citations |
13 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xina Quan | 1 | 0 | 0.34 |
| Junjun Liu | 2 | 0 | 0.34 |
| Thomas Roxlo | 3 | 0 | 0.34 |
| Siddharth Siddharth | 4 | 0 | 0.34 |
| Weyland Leong | 5 | 0 | 0.34 |
| Arthur Muir | 6 | 0 | 0.34 |
| So-Min Cheong | 7 | 0 | 0.34 |
| Anoop Rao | 8 | 0 | 0.34 |