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A deep learning framework for noninvasive fetal ECG signal extraction.

Maisam Wahbah1,2, M Sami Zitouni1, Raghad Al Sakaji3

  • 1College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates.

Frontiers in Physiology
|May 7, 2024
PubMed
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This study introduces a new recurrent neural network framework for accurately detecting fetal electrocardiogram (ECG) R-peaks from abdominal signals. This advancement enables reliable remote fetal monitoring, crucial for maternal and fetal health.

Area of Science:

  • Biomedical Engineering
  • Maternal-Fetal Medicine
  • Signal Processing

Background:

  • Proactive fetal health monitoring is vital for reducing mortality and complications, especially in resource-limited settings or during crises.
  • Existing healthcare systems often struggle to provide continuous fetal monitoring, necessitating innovative solutions for both clinical and home use.

Purpose of the Study:

  • To develop a robust framework for detecting fetal electrocardiogram (ECG) R-peaks directly from composite abdominal signals.
  • To enable direct and fast fetal monitoring in diverse settings, including remote or low-resource environments.

Main Methods:

  • Noninvasive recording of 12-channel abdominal composite signals from 70 pregnant women.
  • Development and application of a recurrent neural network architecture for R-peak detection.
Keywords:
biomedical signal processing algorithmsdeep learningfetal heart ratelong short-term memorynoninvasive fetal electrocardiogram

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  • Validation using subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) testing.

    • Main Results:

    • Achieved an average accuracy of 94.2% in subject-dependent tests and 88.8% in independent tests.
    • Demonstrated a leave-one-subject-out accuracy of 86.7% even during the challenging vernix caseosa layer formation period.
    • Successfully computed fetal heart rate from detected R-peaks, confirming the framework's robustness.

    Conclusions:

    • The proposed recurrent neural network framework effectively detects fetal ECG R-peaks from abdominal signals.
    • This technology holds significant potential for improving maternal and fetal healthcare, enabling continuous monitoring in various conditions.
    • The framework's robustness suggests its applicability in critical healthcare scenarios and advanced fetal monitoring applications.