Name
Abstract | ||
|---|---|---|
Cardiotocography is a measurement technique widely adopted to assess fetal well-being during both antepartum and intra-partum stages. It consists of two simultaneously acquired time-series: fetal heart rate and uterine activity. Since visual inspection analysis is gravely affected by intra- and interobserver variability, recent literature is focusing on automated solutions. In this context, it is essential to provide a robust and accurate estimation of fetal heart rate to prevent from incorrect diagnosis. One of the major challenges is represented by the estimation of FHR baseline. The present paper proposes a novel algorithm capable of accurately estimating the baseline and correctly detecting possible pathological events, like heart rate accelerations or decelerations. In order to provide a thorough metrological characterization of the algorithm performances, we evaluate its accuracy by considering an experimental dataset. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MeMeA.2018.8438706 | 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA) |
Keywords | Field | DocType |
cardiotocography,baseline,fetal heart rate,health monitoring,Myriad filter,variance analysis | Visual inspection,Fetus,Pattern recognition,Computer science,Artificial intelligence,Cardiotocography,Heart rate,Uterine activity | Conference |
ISBN | Citations | PageRank |
978-1-5386-3393-9 | 0 | 0.34 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| guglielmo frigo | 1 | 55 | 10.64 |
| Giada Giorgi | 2 | 71 | 13.30 |