Intrapartum fetal heart rate classification from trajectory in Sparse SVM feature space

Abstract

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.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

“…[61][62][63]71,98,104,110,113,115,[117][118][119]128,134,141,142,145,147,149,155,157,158,169], 117 (82.4%) high ROB studies [29-37,39-60,64-70,72-97,99-103,105-109,111,112,114,116,120-123, 125-127,129-133,135-140,143,144,146,148, 150-154,156,159-168,170], and 1 (0.7%) unclear ROB study (Table 3) [124]. Among the low ROB studies, the categories of…”

“…26/142, 18.3%), consisted of the LR (8/77, 10%) [38,61-64, 71,96,98,104], non-LR (15/50, 30%)[63,110,113,115,[117][118][119]124,128,134,141,142,145,147,155], and both algorithms(3/15, 20%) …”

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis (Preprint)

“…In addition, more non-LR algorithms (13/20, 65%) were applied for the outcome category of in vitro fertilization than for the LR algorithm. [38,63,115,118,119,141,169], fetal distress (5/24, 21%) [71,113,128,145,155], in vitro fertilization (4/24, 17%) [61,110,134,158], gestational diabetes (2/24, 8%) [104,157], cesarean section (CS; 2/24, 8%) [117,142], obstetric labor (1/24, 4%) [62], pregnancy-induced hypertension (1/24, 4%) [147], central nervous system malformations (1/24, 4%) [149], and others (1/24, 4%) [98]. ROB is also described across the studies in Table 3 and Figure 2.…”

“…The corresponding signaling questions for each term and the answers for each study are described in Multimedia Appendix 1. Low ROB studies were the fewest in the analysis domain (26/142, 18.3%), consisted of the LR (8/77, 10%) [38,61-64, 71,96,98,104], non-LR (15/50, 30%) [63,110,113,115,[117][118][119]124,128,134,141,142,145,147,155], and both algorithms (3/15, 20%) [157,158,169]. In the analysis domain, the fewest low ROB studies that achieved the minimum events per variable (EPV) consisted of LR (35/77, 45%) and non-LR (31/50, 62%) prediction studies.…”

Generalized Legendre transform multifractal formalism for nonconcave spectrum estimation

“…Multifractal analysis. Nowadays, multifractal analysis has become a standard signal processing tool, fruitfully used for the analysis of highly irregular signals in a wide range of applications, including biomedical [1], geophysics [2], finance [3], and art investigation [4], to name but a few. It essentially amounts to estimating the multifractal spectrum D(h), which quantifies the distribution of points with a given regularity exponent h. This spectrum characterizes signals in terms of its regularity, and is thus used in standard classification or modeling tasks.…”