Maternal and Fetal Electrocardiograms Separation Algorithm
The Development and Validation of Maternal and Fetal Electrocardiograms (ECG) Separation Algorithm Based on Artificial Intelligence Application
I.M. Sechenov First Moscow State Medical University
350 participants
Feb 12, 2026
INTERVENTIONAL
Conditions
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 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.
Eligibility
Inclusion Criteria5
- 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 Criteria6
- 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).
- \. Patient's refusal to continue participation in the study.
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Interventions
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.
Locations(1)
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NCT07518550