Maternal and Fetal Electrocardiograms Separation Algorithm
ConditionPregnancy Related
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexFemale
Age18-55
SponsorI.M. Sechenov First Moscow State Medical University
About this trial
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 criteria
This trial does not accept healthy volunteersQualifiers
Age over 18 years
Recordings obtained during the second or third trimester of pregnancy
Recording duration of at least 5 minutes
Singleton pregnancy
Disqualifiers
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.);
Trial design
Design model
Single group
Treatments tested in this trial
Maternal and fetal electrocardiograms separation
Other interventionSensors 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.
Treatment groups
350 Participants
are divided into 1 treatment groupGroup A: Electrocardiography registration groupExperimental treatment 1 intervention
Trial outcomes
Primary outcomes
1
Correlation coefficient between automatically extracted fetal heart rates and reference. signals
Сardiotocography (CTG) will be used as a reference.
Time frame
Through study completion, an average of 1 yearSecondary outcomes
1
Signal processing time and computational complexity of the algorithm.
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.
Time frame
Through study completion, an average of 1 year2
Accuracy of R-peak detection: number of correctly identified fetal heartbeats (sensitivity) and number of false positives (specificity).
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.
Time frame
Through study completion, an average of 1 year3
Proportion of rejected or invalid segments where the algorithm failed to reliably extract fetal data.
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.
Time frame
Through study completion, an average of 1 yearSponsors and contacts
Click on the lead sponsor to view all of their trials.
Source ClinicalTrials.gov