Pregnancy RelatedNCT07518550
Maternal and Fetal Electrocardiograms Separation Algorithm
A study testing Maternal and fetal electrocardiograms separation in people with Pregnancy Related, run by I.M. Sechenov First Moscow State Medical University at 1 site in Russia.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 to 55 years, women only
- Conditions the study is for
- Pregnancy Related
- What is being tested?
- Maternal and fetal electrocardiograms separation
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 350 participants
- Where
- 1 siteRussia
- Study dates
- 12 Feb 2026to 30 May 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 55 years
- Women only
- No healthy volunteers
- Adult
Who may be able to join
- 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
Who cannot join
- 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).
- 1. Patient's refusal to continue participation in the study.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Russia1 site, 1 recruiting
- Recruiting
V.F. Snegirev Clinic of Obstetrics and Gynecology of I.M. Sechenov First Moscow State Medical University
Moscow, 119435
Philipp Yu Kopylov, Prof.
About this study
Official title: 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.
Read the 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
- 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.
- 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.
- 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.
- Evaluate the diagnostic parameters of the algorithm for assessing the fetal condition
- Conditions
- Pregnancy Related
- Being tested
- Maternal and fetal electrocardiograms separation
- Keywords
- pregnancy, maternal and fetal electrocardiograms separation, 2-3 trimester, abdominal ECG signals, artificial intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Maternal and fetal electrocardiograms separationOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Electrocardiography registration group | Experimental | Pregnant women in the 2nd to 3rd trimester. Receives: Other: Maternal and fetal electrocardiograms separation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Correlation coefficient between automatically extracted fetal heart rates and reference. signals
Сardiotocography (CTG) will be used as a reference.
Measured: Through study completion, an average of 1 year
Secondary outcomes (3)
Signal processing time and computational complexity of the algorithm.
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.
Measured: 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).
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.
Measured: Through study completion, an average of 1 year
Proportion of rejected or invalid segments where the algorithm failed to reliably extract fetal data.
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.
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Apr 2026
- Study start12 Feb 2026
- Primary completion20 Jan 2027 (estimated)
- Study completion30 May 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pregnancy RelatedStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 350 participants (estimated)
Sponsor and investigators
- Lead sponsor
- I.M. Sechenov First Moscow State Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Philipp Yu Kopylov, Prof., Principal investigator, I.M. Sechenov First Moscow State Medical University (Sechenov University)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07518550
- Sponsor's protocol ID
- KPh01.2026
- Status last verified
- Mar 2026
- First submitted
- 27 Mar 2026
- Last updated
- 8 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Development and Validation of Maternal and Fetal Electrocardiograms (ECG) Separation Algorithm Based on Artificial Intelligence Application
- ClinicalTrials.gov ID
- NCT07518550
- Acronym / sponsor ID
- KPh01.2026
- Conditions
- Pregnancy Related
- Keywords
- pregnancy, maternal and fetal electrocardiograms separation, 2-3 trimester, abdominal ECG signals, artificial intelligence
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 350 participants (estimated)
- Lead sponsor
- I.M. Sechenov First Moscow State Medical UniversityUniversity, hospital or other
- Study start
- 12 Feb 2026 (actual)
- Primary completion
- 20 Jan 2027 (estimated)
- Study completion
- 30 May 2028 (estimated)
- First posted
- 8 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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
- 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.
- 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.
- 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.
- Evaluate the diagnostic parameters of the algorithm for assessing the fetal condition
Study design
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Electrocardiography registration group | Experimental | Other: Maternal and fetal electrocardiograms separationPregnant women in the 2nd to 3rd trimester. |
Who may qualify
- Ages
- 18 to 55 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (6 listed)
- 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).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Maternal and fetal electrocardiograms separationSensors 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. | Other | Electrocardiography registration group |
Primary outcome
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.
3 secondary outcomes
Signal processing time and computational complexity of the algorithm.
Time frame: 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
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Apr 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Philipp Yu Kopylov, Prof. principal investigator
I.M. Sechenov First Moscow State Medical University (Sechenov University)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Maternal and fetal electrocardiograms separation in people with Pregnancy Related, run by I.M. Sechenov First Moscow State Medical University at 1 site in Russia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 8 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Russia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Philipp Yu Kopylov, Prof. (+7-903-687-72-64, kopylov_f_yu@staff.sechenov.ru), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Correlation coefficient between automatically extracted fetal heart rates and reference. signals (time frame: Through study completion, an average of 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Apr 2026.