Fetal Evaluation at Term Using Statistical ECG Signal Processing (FETUSES) (FETUSES)

May 20, 2020 updated by: Hospices Civils de Lyon

Background

The fetal heart rate (FHR) is commonly monitored during labor to detect early fetal acidosis. FHR variability is traditionally investigated using Fourier transform, however, fetal conditions differ from adults (higher frequency, widely instable signal) modifying spectrum repartition along frequencies, making this approach inappropriate. New mathematic approach, including Multifractal analysis, adaptative multiscale complexity analysis, nearest-neighbor based wavelet entropy rate measures, scattering transform, may identify parameters associated with fetal acidosis. Modern classification system, driven by the data, based on these relevant parameters should help to dsiccriminate the fetus at risk for acidosis and help the clinician in decision making for the management and termination of labor. Analysing 20 to 30 minutes sliding windows with these different methods in a limited number of cases (foetus born with acidosis) and controls (foetus without acidosis) should lead to identify relevant parameters, which will be submitted to these classification tools. For external validation, a large database, including more than 4500 FHR recording documented with foetal outcomes, will be used to evaluate the performance of the combination of parameters identify and classification system used to discriminate foetuses at risk for short term acidosis or not, in order to give a relevant information to the clinician before acidosis occurs, to make the appropriate decision.

Study Overview

Status

Terminated

Study Type

Observational

Enrollment (Actual)

3563

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 45 years (Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

Female

Sampling Method

Non-Probability Sample

Study Population

All fetuses with STAN surveillance during labor.

Description

--- Inclusion Criteria:

  • women with singleton pregnancy and fetal ECG recording using a scalp electrode, with recording lasting more than 60 minutes, with less than 20 % of missing data and less than 20 minutes between the end of recording and birth.

    • Exclusion Criteria:
  • Women with recirdin lasting less than 60 minutes, with more than 20 % of missing data, or more than 20 minutes between the end of recording and birth.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Hurst parameter comes from multifractal analysis
Time Frame: Day 0 (during 20 to 30 minutes sliding windows during labor)
Hurst parameter and P-leader comes from multifractal analysis of fetal ECG that quantify fetal heart rate variability.
Day 0 (during 20 to 30 minutes sliding windows during labor)
P-leader comes from multifractal analysis
Time Frame: Day 0 (during 20 to 30 minutes sliding windows during labor)
Hurst parameter and P-leader comes from multifractal analysis of fetal ECG that quantify fetal heart rate variability.
Day 0 (during 20 to 30 minutes sliding windows during labor)

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Scattering transform
Time Frame: Day 0 (during 20 to 30 minutes sliding windows during labor)
The scattering transform is a nonlinear extension of the underlying wavelet transform used for multifractal analysis and is another way to quantify fetal heart rate variability
Day 0 (during 20 to 30 minutes sliding windows during labor)
Entropy rate
Time Frame: Day 0 (during 20 to 30 minutes sliding windows during labor)
A k-nearest neighbor procedure yields estimates for entropy rates is another method to quantify fetal heart rate variability
Day 0 (during 20 to 30 minutes sliding windows during labor)
adaptative multiscale complexity analysis
Time Frame: Day 0 (during 20 to 30 minutes sliding windows during labor)
THE FHR signal is separated in the lower number of lines to fullfile the whole signal. The number and longer of segment is data driven, and is a direct reflect of the signal complexity
Day 0 (during 20 to 30 minutes sliding windows during labor)
Support vector machine (SVM) classification
Time Frame: Day 0 (during 20 to 30 minutes sliding windows during labor)
SVM permits to select a small number of relevant features from the different method of analysis previously described and to discriminate fetuses with and without acidosis
Day 0 (during 20 to 30 minutes sliding windows during labor)

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start

January 1, 2012

Primary Completion (Actual)

October 1, 2013

Study Completion (Actual)

October 1, 2013

Study Registration Dates

First Submitted

July 13, 2016

First Submitted That Met QC Criteria

July 13, 2016

First Posted (Estimate)

July 18, 2016

Study Record Updates

Last Update Posted (Actual)

May 21, 2020

Last Update Submitted That Met QC Criteria

May 20, 2020

Last Verified

May 1, 2020

More Information

Terms related to this study

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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