Name
Abstract | ||
|---|---|---|
Cardiovascular data recording by implantable sensor modules exhibits a number of advantages over extracorporeal standard approaches. Implantable sensors feature their benefits in particular for high risk patients suffering from chronic heart diseases, because diagnosis can be combined with therapy in a closed loop system. Nevertheless, the measured photoplethysmographic signals reveal different kinds of noise and artifacts. There are several parametric and non-parametric mathematical techniques that try to achieve optimality and generality in estimating the actual signal out of its noisy representation. The determination of blood oxygen saturation and pulse transit time requires one of these mathematical techniques for gaining the exact position and magnitude of maxima and minima in the photoplethysmograph. A robust wavelet algorithm resolves the difficulties arising from physiological data. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IEMBS.2011.6091192 | 2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
sensors,time frequency analysis,noise,noise reduction,noise measurement,data analysis,oxygen saturation,photoplethysmography,cardiovascular system | Noise reduction,Computer vision,Noise measurement,Computer science,Photoplethysmogram,Maxima and minima,Electronic engineering,Pulse (signal processing),Parametric statistics,Time–frequency analysis,Artificial intelligence,Wavelet | Conference |
Volume | ISSN | Citations |
2011 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dominic Ruh | 1 | 4 | 1.71 |
| Jens Fiala | 2 | 0 | 0.34 |
| Hans Zappe | 3 | 2 | 1.43 |
| Andreas Seifert | 4 | 8 | 3.29 |