Pregnancy Related
Conditions
Keywords
pregnancy, maternal and fetal electrocardiograms separation, 2-3 trimester, abdominal ECG signals, artificial intelligence
Brief 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.
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
OTHERMaternal 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.
Sponsors
I.M. Sechenov First Moscow State Medical University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to 55 Years
Healthy volunteers
No
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).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Correlation coefficient between automatically extracted fetal heart rates and reference. signals | Through study completion, an average of 1 year | Сardiotocography (CTG) will be used as a reference. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Signal processing time and computational complexity of the algorithm. | Through study completion, an average of 1 year | 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. |
| Accuracy of R-peak detection: number of correctly identified fetal heartbeats (sensitivity) and number of false positives (specificity). | Through study completion, an average of 1 year | 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. |
| Proportion of rejected or invalid segments where the algorithm failed to reliably extract fetal data. | Through study completion, an average of 1 year | 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. |
Countries
Russia
Contacts
CONTACTPhilipp Yu Kopylov, Prof.
CONTACTSheron R Rakhamimova, PhD Student
PRINCIPAL_INVESTIGATORPhilipp Yu Kopylov, Prof.
I.M. Sechenov First Moscow State Medical University (Sechenov University)
Outcome results
None listed