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.

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

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.