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Computerized Antepartum Monitoring Using Non-invasive Fetal Ecg for High Risk Pregnancy

Computerized Antepartum Monitoring Using Non-invasive Fetal Ecg for High Risk Pregnancy

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04186975
Enrollment
500
Registered
2019-12-05
Start date
2019-12-01
Completion date
2023-12-01
Last updated
2019-12-05

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Cardiotocography

Keywords

FETAL MONITORING, CARDIOTOCOGRAPHY

Brief summary

The long term aim of this research is to evaluate a portable NI-FECG (Non-invasive fetal ECG) monitor (Holter NI-FECG) which can be used for regular remote assessment of fetal health in pregnancies at risk or to follow-up on treatments. The elaboration of a NI-FECG Holter device will offer new opportunities for fetal diagnosis and remote monitoring of problematic pregnancies because of its low-cost, non-invasiveness, portability and minimal set-up requirements.

Detailed description

Pregnant patients that are of gestational age in which fetal heart rate monitoring is recommended and feasible will be enrolled to this cohort study. Each patient will be monitored via conventional fetal heart rate monitoring in addition to the NI-FECG method and both methods will be directly compared. Each patient will be her own control. NI-FECG is a non-invasive method of fetal monitoring' thus no ethical issues are relevant. Nevertheless, each patient will sign informed consent before participating in the study.

Interventions

DEVICENon-Invasive fetal ECG

Non-Invasive fetal ECG

Fetal heart rate monitor

Sponsors

Technion, Israel Institute of Technology
CollaboratorOTHER
Rambam Health Care Campus
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
FEMALE
Age
18 Years to 50 Years
Healthy volunteers
Yes

Inclusion criteria

* Singleton pregnancies. * low-risk pregnancy: women from the post-date clinic (after 40 weeks' gestation) in which we perform routinely Non stress test and ultrasound. * High-risk pregnancy: women who are hospitalized for different indications: IUGR, diabetes, hypertension, non-reassuring fetal heart rate, Decreased fetal movements

Exclusion criteria

* Non singleton pregnancies. * Do not want to participate in the study

Design outcomes

Primary

MeasureTime frameDescription
Identification of abnormal fetal heart rate from NI-FECG2 yearsTo compare the clinical interpretation of the NI-FECG obtained fetal heart rate trace to the fetal heart rate interpretation from the fetal heart rate obtained from conventional CTG. This will involve blinded reading and scoring of FHR obtained from the NI-FECG and CTG from a panel of expert clinician.
Computerized NI-FECG for the prediction of abnormal FHR traces2 yearsTo compare the computerized analysis of the FHR trace obtained using NI-FECG to the clinician visual interpretation of the FHR trace obtained using CTG (usual care). This will involve the implementation of algorithms that can detect standard14 and new features assessing the Fetal HRV (FHRV) and the elaboration of a machine learning model which can predict abnormal traces from these features
Comparison between computerized CTG and NI-FECG4 yearsTo compare the predictive power of computerized CTG versus computerized NI-FECG for the assessment of abnormal traces. For that purpose, a machine learning model will be trained (1) on features extracted from the FHR trace obtained using CTG and (2) on features extracted from the FHR obtained using the NI-FECG trace
Develop a portable NI-FECG monitor for remote fetal monitoring.4 yearsTo develop a portable NI-FECG monitor which can be used to record the fetal ECG at the patient's home. The monitor will transfer the data to a remote server where source separation will be performed to extract the fetal ECG. Algorithms implemented for extracting characteristic features and the machine learning model will be run to predict whether the traces are normal or abnormal. The elaboration of such algorithm is particularly relevant for resource constrained region where medical experts is scarce.

Contacts

Primary ContactOren Grunwald, MD
oren.grunwald@gmail.com+972506914415

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026