Maternal and Fetal Electrocardiograms Separation Algorithm

April 2, 2026 updated by: I.M. Sechenov First Moscow State Medical University

The Development and Validation of Maternal and Fetal Electrocardiograms (ECG) Separation Algorithm Based on Artificial Intelligence Application

Effective monitoring of fetal heart activity during the second and third trimesters remains a vital challenge in perinatal medicine. This study proposes an adaptive algorithm for extracting the fetal electrocardiograms signal from abdominal ECG in pregnant women, considering the physiological characteristics of each trimester. Utilizing modern machine learning methods, independent component analysis, and data from wearable textile electrodes. The goal is to enhance the accuracy and reliability of automatic signal separation. A dataset of 300 recordings will be collected and analyzed. The resulting algorithm will enable rapid and precise detection of fetal heartbeats. To validate the algorithm, 50 patients will be recruited separately.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Research Objective Development and validation of an algorithm for separating maternal and fetal electrocardiographic signals based on non-invasive abdominal ECG in pregnant women during the second and third trimesters of gestation.

Research Tasks

  1. Perform abdominal ECG recordings in pregnant women using a non-invasive technology, ensuring standardized recording conditions and accounting for gestational age. Each recording should contain at least 5-10 minutes of continuous signals, providing sufficient data volume for analysis and algorithm training.
  2. Analyze features of abdominal ECG signals at various gestational stages, including morphology of maternal and fetal rhythms, their degree of overlap, and the influence of physiological factors. Compare findings with clinical history and other diagnostic methods.
  3. Develop and adapt an algorithm for separating maternal and fetal electrocardiographic signals, considering the specific features during the second and third trimesters, to enhance the accuracy of fetal cardiac activity diagnosis based on machine learning.
  4. Evaluate the diagnostic parameters of the algorithm for assessing the fetal condition

Study Type

Interventional

Enrollment (Estimated)

350

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

  • Name: Sheron R Rakhamimova, PhD Student
  • Phone Number: +7-909-933-54-54
  • Email: rshery2631@yandex.ru

Study Locations

  • Russia
      • Moscow, Russia, 119435
        • Recruiting
        • V.F. Snegirev Clinic of Obstetrics and Gynecology of I.M. Sechenov First Moscow State Medical University
        • Contact:
          • Philipp Yu Kopylov, Prof.

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

  • Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Age over 18 years
  • Recordings obtained during the second or third trimester of pregnancy
  • Recording duration of at least 5 minutes
  • Singleton pregnancy
  • Signed informed consent

Exclusion Criteria:

  • Age under 18 years;
  • Multiple pregnancy;
  • Recent medical procedures or interventions that could affect the quality of electrocardiographic data;
  • Severe maternal conditions (e.g., severe eclampsia, shock, severe organ failure, etc.);
  • Severe fetal conditions (e.g., significant hypoxia, severe placental-fetal syndrome, and other life-threatening states).

Exclusion criteria:

1. Patient's refusal to continue participation in the study.

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

  • Primary Purpose: Other
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Electrocardiography registration group
Pregnant women in the 2nd to 3rd trimester.
Other: Maternal and fetal electrocardiograms separation
Sensors are attached to the pregnant woman's abdomen on pre-prepared sites, and data are recorded for at least 10 minutes. Afterwards, the ECG signals are processed to remove noise.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Correlation coefficient between automatically extracted fetal heart rates and reference. signals
Time Frame: Through study completion, an average of 1 year
Сardiotocography (CTG) will be used as a reference.
Through study completion, an average of 1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Signal processing time and computational complexity of the algorithm.
Time Frame: Through study completion, an average of 1 year
The signal processing time refers to the duration required for the algorithm to analyze and process the input signals, including steps such as filtering, noise removal, feature extraction, and data alignment.
Through study completion, an average of 1 year
Accuracy of R-peak detection: number of correctly identified fetal heartbeats (sensitivity) and number of false positives (specificity).
Time Frame: Through study completion, an average of 1 year
The accuracy of R-peak detection refers to the algorithm's ability to correctly identify fetal heartbeats within the recorded signals. Sensitivity (true positive rate) indicates the proportion of actual fetal heartbeats that were correctly detected by the algorithm. Specificity (true negative rate or false positive rate) reflects the number of false detections, i.e., instances where non-heartbeat signals were incorrectly identified as fetal heartbeats. High sensitivity and specificity are essential for reliable fetal heart rate monitoring, minimizing missed beats and false alarms.
Through study completion, an average of 1 year
Proportion of rejected or invalid segments where the algorithm failed to reliably extract fetal data.
Time Frame: Through study completion, an average of 1 year
The proportion of rejected or invalid segments refers to the percentage of data segments in which the algorithm was unable to reliably extract fetal heart rate information. These segments are typically excluded from analysis due to poor signal quality, noise, or other artifacts that prevent accurate detection of fetal data.
Through study completion, an average of 1 year

Collaborators and Investigators

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

Sponsor

I.M. Sechenov First Moscow State Medical University

Investigators

  • Principal Investigator: Philipp Yu Kopylov, Prof., I.M. Sechenov First Moscow State Medical University (Sechenov University)

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 (Actual)

February 12, 2026

Primary Completion (Estimated)

January 20, 2027

Study Completion (Estimated)

May 30, 2028

Study Registration Dates

First Submitted

March 27, 2026

First Submitted That Met QC Criteria

April 2, 2026

First Posted (Actual)

April 8, 2026

Study Record Updates

Last Update Posted (Actual)

April 8, 2026

Last Update Submitted That Met QC Criteria

April 2, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • KPh01.2026

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

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.

Clinical Trials on Pregnancy Related

Clinical Trials on Maternal and fetal electrocardiograms separation

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Azerbaijan

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe