title: P-leader multifractal analysis and sparse SVM for intrapartum fetal acidosis detection.
abstract: Interpretation and analysis of intrapartum fetal heart rate, enabling early detection of fetal acidosis, remains a challenging signal processing task. Among the many strategies that were used to tackle this problem, scale-invariance and multifractal analysis stand out. Recently, a new and promising variant of multifractal analysis, based on p-leaders, has been proposed. In this contribution, we use sparse support vector machines applied to p-leader multifractal features with a double aim: Assessment of the features actually contributing to classification; Assessment of the contribution of non linear features (as opposed to linear ones) to classification performance. We observe and interpret that the classification rate improves when small values of the tunable parameter p are used.
authors.forename: R
authors.initials: R
authors.lastname: Leonarduzzi
authors.forename: J
authors.initials: J
authors.lastname: Spilka
authors.forename: J
authors.initials: J
authors.lastname: Frecon
authors.forename: H
authors.initials: H
authors.lastname: Wendt
authors.forename: N
authors.initials: N
authors.lastname: Pustelnik
authors.forename: S
authors.initials: S
authors.lastname: Jaffard
authors.forename: P
authors.initials: P
authors.lastname: Abry
authors.forename: M
authors.initials: M
authors.lastname: Doret
mesh_terms: D000138:Acidosis / Q000175:diagnosis*; D019540:Area Under Curve; D005260:Female; D005315:Fetal Diseases / Q000175:diagnosis*; D006340:Heart Rate, Fetal; D006801:Humans; D016014:Linear Models; D015999:Multivariate Analysis; D011247:Pregnancy; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine