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In this paper, we present an effective solutionBlind separation source for analyzing and extracting electrocardiogram (ECGElectrocardiogram (ECG)) signals from the abdomen and thorax of pregnant women, with the primary aim of isolating fetal ECGFetal ECG (fECG) and maternal ECGElectrocardiogram (ECG) (mECG) signals. To solve problems such as the low amplitude of the fetal ECGFetal ECG, the various noise sources during signal acquisition, and R-wave overlap, we developed a new method based on blind sourceBlind separation source separation techniques. This method uses independent component analysis algorithms to accurately detect and extract fECG and mECG signals from abdominal and thorax data. To validate our solution, we carried out experiments using a reliable database specifically designed to evaluate fECG extraction algorithms. In addition, to demonstrate real-time applicability, we implemented our method on an embedded board connected to electronic modules of the pregnant woman’s body temperature. This configuration also includes the transmission of fetal and maternal data to physicians for diagnosis of fetal and maternal conditions. The accuracy of our method in isolating fECG and mECG signals under difficult conditions is demonstrated by our results, as well as by the calculation of heart rates (fBPM and mBPM). This method offers good potential for improving fetal monitoring and maternal health care during childbirth.

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Development and Implementation of a Blind Source Separation Algorithm to Extract the Fetal Electrocardiographic Signal in Real-Time

  • 会议论文

  • 发表日期：

    2025年

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  • Mohcin Mekhfioui,
  • Aziz Benahmed,
  • Ahmed Chebak,
  • Rachid Elgouri,
  • Laamari Hlou

摘要

In this paper, we present an effective solutionBlind separation source for analyzing and extracting electrocardiogram (ECGElectrocardiogram (ECG)) signals from the abdomen and thorax of pregnant women, with the primary aim of isolating fetal ECGFetal ECG (fECG) and maternal ECGElectrocardiogram (ECG) (mECG) signals. To solve problems such as the low amplitude of the fetal ECGFetal ECG, the various noise sources during signal acquisition, and R-wave overlap, we developed a new method based on blind sourceBlind separation source separation techniques. This method uses independent component analysis algorithms to accurately detect and extract fECG and mECG signals from abdominal and thorax data. To validate our solution, we carried out experiments using a reliable database specifically designed to evaluate fECG extraction algorithms. In addition, to demonstrate real-time applicability, we implemented our method on an embedded board connected to electronic modules of the pregnant woman’s body temperature. This configuration also includes the transmission of fetal and maternal data to physicians for diagnosis of fetal and maternal conditions. The accuracy of our method in isolating fECG and mECG signals under difficult conditions is demonstrated by our results, as well as by the calculation of heart rates (fBPM and mBPM). This method offers good potential for improving fetal monitoring and maternal health care during childbirth.