Statistical Modeling of Fetal Heart Rate Variability

Jarisch, Wolfram; Detwiler, John S.

doi:10.1109/tbme.1980.326580

Cited by 15 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It thus constitutes an important public health stake to reduce the number of unnecessary operative deliveries, which motivates significant research efforts i) to automatize the computation of robust features from digitized CTG and ii) to produce sound statistical characterization of CTG and F-HRV beyond the essentially morphological (or geometric) FIGO criteria (cf. e.g., [5], [6] or [7], [8] for tentative reviews).…”

Section: Introductionmentioning

confidence: 99%

Scattering Transform for Intrapartum Fetal Heart Rate Variability Fractal Analysis: A Case-Control Study

Chudáček

Andén

Mallat

et al. 2014

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

Abstract-Intrapartum fetal heart rate monitoring, aiming at early acidosis detection, constitutes an important public health stake. Scattering Transform is proposed here as a new tool to analyze intrapartum fetal heart rate variability. It consists of a non linear extension of the underlying Wavelet Transform, that thus preserves its multiscale nature. Applied to a Fetal Heart Rate (FHR) signal database constructed in a French academic hospital, the Scattering Transform is shown to permit to efficiently measure scaling exponents characterizing the fractal properties of intrapartum fetal heart rate temporal dynamics, that relate not only to the sole covariance (correlation scaling exponent) but also to the full dependence structure of data (intermittency scaling exponent). Such exponents are found to satisfactorily discriminate temporal dynamics of Healthy subjects (from that of Non Healthy ones) and to emphasize the role of the highest frequencies (around and above 1Hz) in intrapartum fetal heart rate variability. This permits to achieve satisfactory classification performance, that improves on those obtained from the analysis of International Federation of Gynecology and Obstetrics (FIGO) criteria, notably by classifying as Healthy a number of subjects that were incorrectly classified as Non Healthy by classical clinically-used FIGO criteria. Combined to obstetrician annotations, these scaling exponents enables us to sketch a typology of these FIGO-False Positive subjects. Also, they permit to monitor the evolution along time of the intrapartum health status of the fetuses and to estimate an optimal detection time-frame.

show abstract

Section: Introductionmentioning

confidence: 99%

Scattering Transform for Intrapartum Fetal Heart Rate Variability Fractal Analysis: A Case-Control Study

Chudáček

Andén

Mallat

et al. 2014

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

show abstract

“…5f and 5i, in which the elimination of the transitory acceleration is seen to bring about a significant fall in the intensity of F 2 , together with a displacement towards a higher frequency ränge. Their absence may be taken äs a sign of anencephaly or incipient intrauterine foetal death [4]. 5f with Figs.…”

Section: Discussionmentioning

confidence: 99%

Homomorphic Deconvolution Applied to Foetal Heart Rate Signals - Homomorphische Entkonvolution auf Foetus-Herzrhythmussignale angewandt

Ramos-Hernández¹

1982

Biomedizinische Technik/Biomedical Engineering

View full text Add to dashboard Cite

“…These oscillations are called longterm variability [4]. Essential element used to generate short-and longterm variability is the autoregressive filter [7], [8]. The idea of the autoregressive method is based on assumption that signal can be represented as a white noise signal passed through the filter of the following transfer function:…”

Section: Evaluation Of Fetal Heart Rate Baseline Estimation Methods Usmentioning

confidence: 99%

Evaluation of Fetal Heart Rate Baseline Estimation Method Using Testing Signals Based on a Statistical Model

Kupka

Wróbel

Jeżewski

et al. 2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

Computer-aided fetal monitoring is based on automated analysis of the fetal heart rate (FHR) variability. The first and the main step in the automated signal interpretation is the estimation of the so called FHR baseline. There are various algorithms for baseline estimation, of different efficiency. For its evaluation, the method of modeling of FHR signal based on the preset baseline component has been developed. The best algorithm is expected to provide the same baseline as the component baseline used to model the FHR signal. Generated signals were used to compare the baselines that have been estimated by two algorithm: the first one relying on artificial neural networks and the classical one using nonlinear filtering of FHR signal.

show abstract

Statistical Modeling of Fetal Heart Rate Variability

Cited by 15 publications

References 13 publications

Scattering Transform for Intrapartum Fetal Heart Rate Variability Fractal Analysis: A Case-Control Study

Scattering Transform for Intrapartum Fetal Heart Rate Variability Fractal Analysis: A Case-Control Study

Homomorphic Deconvolution Applied to Foetal Heart Rate Signals - Homomorphische Entkonvolution auf Foetus-Herzrhythmussignale angewandt

Evaluation of Fetal Heart Rate Baseline Estimation Method Using Testing Signals Based on a Statistical Model

Contact Info

Product

Resources

About