Name
Abstract | ||
|---|---|---|
Cuff-less blood pressure estimation technology is useful for cardiovascular disease monitoring. However, without calibration, cuff-less blood pressure estimation is hard to achieve clinical acceptable performance. The traditional methods are always calibrated with retraining. With the increases of the parameters number, the cost of model retraining increases several times. So we propose a novel blood pressure estimation method, which can be calibrated with reference inputs rather than with retraining. The experiment results suggest that the method we proposed can achieve clinical performance (SBP:-0.004 +/- 5.869 mmHg, DBP:-0.004 +/- 4.511 mmHg) with low calibration cost. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857373 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Computer vision,Cardiovascular disease monitoring,Convolution,Cuff,Computer science,Feature extraction,Artificial intelligence,Blood pressure,Calibration | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenqi Li | 1 | 4 | 3.11 |
| Cong Yan | 2 | 3 | 2.09 |
| Wei Zhao | 3 | 0 | 0.34 |
| Jing Hu | 4 | 2 | 1.36 |
| Dongya Jia | 5 | 4 | 4.80 |
| Hongmei Wang | 6 | 31 | 13.44 |
| Tianyuan You | 7 | 1 | 1.07 |