The Development and Validation of Maternal and Fetal Electrocardiograms (ECG) Separation Algorithm Based on Artificial Intelligence Application
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 350
- 试验地点
- 1
- 主要终点
- Correlation coefficient between automatically extracted fetal heart rates and reference. signals
研究概览
简要总结
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.
详细描述
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
- 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.
- 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.
- 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.
- Evaluate the diagnostic parameters of the algorithm for assessing the fetal condition
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Other
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 55 Years（Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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.
研究组 & 干预措施
Electrocardiography registration group
Pregnant women in the 2nd to 3rd trimester.
干预措施: Maternal and fetal electrocardiograms separation (Other)
结局指标
主要结局
Correlation coefficient between automatically extracted fetal heart rates and reference. signals
时间窗: Through study completion, an average of 1 year
Сardiotocography (CTG) will be used as a reference.
次要结局
- Signal processing time and computational complexity of the algorithm.(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).(Through study completion, an average of 1 year)
- Proportion of rejected or invalid segments where the algorithm failed to reliably extract fetal data.(Through study completion, an average of 1 year)