Intrapartum fetal heart rate classification from trajectory in Sparse SVM feature space
| Autor: | Patrice Abry, Jordan Frecon, Muriel Doret, Jiří Spilka, Nelly Pustelnik, Roberto Leonarduzzi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Fetal acidosis
Multivariate statistics Multivariate analysis Support Vector Machine Computer science Feature vector Machine learning computer.software_genre Wavelet Fetus Pregnancy medicine Humans business.industry Pattern recognition Fetal health Heart Rate Fetal medicine.disease Support vector machine Fetal heart rate embryonic structures Multivariate Analysis Trajectory Female Artificial intelligence business Acidosis computer |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | Intrapartum fetal heart rate (FHR) constitutes a prominent source of information for the assessment of fetal reactions to stress events during delivery. Yet, early detection of fetal acidosis remains a challenging signal processing task. The originality of the present contribution are three-fold: multiscale representations and wavelet leader based multifractal analysis are used to quantify FHR variability ; Supervised classification is achieved by means of Sparse-SVM that aim jointly to achieve optimal detection performance and to select relevant features in a multivariate setting ; Trajectories in the feature space accounting for the evolution along time of features while labor progresses are involved in the construction of indices quantifying fetal health. The classification performance permitted by this combination of tools are quantified on a intrapartum FHR large database (≌ 1250 subjects) collected at a French academic public hospital. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|