Name
Abstract | ||
|---|---|---|
This study experimentally investigates the relationships between central cardiovascular variables and oxygen uptake based on nonlinear analysis and modeling. Ten healthy subjects were studied using cycle-ergometry exercise tests with constant workloads ranging from 25 Watt to 125 Watt. Breath by breath gas exchange, heart rate, cardiac output, stroke volume and blood pressure were measured at each stage. The modeling results proved that the nonlinear modeling method (Support Vector Regression) outperforms traditional regression method (reducing Estimation Error between 59% and 80%, reducing Testing Error between 53% and 72%) and is the ideal approach in the modeling of physiological data, especially with small training data set. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-92219-3_15 | BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES |
Keywords | Field | DocType |
Cardiovascular system,Nonlinear modeling,Cardiovascular responses to Exercise,Machine learning | Data mining,Stroke volume,Nonlinear system,Regression,Simulation,Computer science,Watt,Support vector machine,Blood pressure,Heart rate,Statistics,Cardiac output | Conference |
Volume | ISSN | Citations |
25 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lu Wang | 1 | 2 | 0.95 |
| Steven W. Su | 2 | 210 | 45.84 |
| Gregory S H Chan | 3 | 9 | 3.46 |
| Branko G. Celler | 4 | 502 | 81.99 |
| Teddy M. Cheng | 5 | 168 | 14.31 |
| Andrey V. Savkin | 6 | 1431 | 178.60 |