Maternal and Fetal Electrocardiograms Separation Algorithm

Official title

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

Overview

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

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

Interventions

• 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.

Primary outcome measures

• Correlation coefficient between automatically extracted fetal heart rates and reference. signals [Time frame: Through study completion, an average of 1 year]

Secondary outcome measures (3)

• 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]

Eligibility criteria

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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

N/A

Model

Single group

Masking

Open label

Primary purpose

Other

Study locations

Russia · 1 center

• V.F. Snegirev Clinic of Obstetrics and Gynecology of I.M. Sechenov First Moscow State Medi — Moscow

Identifiers

NCT: NCT07518550 · KPh01.2026

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗