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Maternal and Fetal Electrocardiograms Separation Algorithm
Sponsor: I.M. Sechenov First Moscow State Medical University
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.
Official title: The Development and Validation of Maternal and Fetal Electrocardiograms (ECG) Separation Algorithm Based on Artificial Intelligence Application
Key Details
Gender
FEMALE
Age Range
18 Years - 55 Years
Study Type
INTERVENTIONAL
Enrollment
350
Start Date
2026-02-12
Completion Date
2028-05-30
Last Updated
2026-04-08
Healthy Volunteers
No
Conditions
Interventions
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.
Locations (1)
V.F. Snegirev Clinic of Obstetrics and Gynecology of I.M. Sechenov First Moscow State Medical University
Moscow, Russia