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A Deep Learning-Guided Ensemble Empirical Mode Decomposition Method for Single-Channel Fetal Electrocardiogram

Xiaojian Xu1,2, Yifan Zhang1,2, Yufei Rao2

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning method automatically extracts fetal ECG signals from abdominal recordings, improving accuracy for home monitoring and fetal health assessment. This technique enhances signal clarity, aiding in early detection of fetal distress.

Keywords:
ensemble empirical mode decomposition (EEMD)fetal electrocardiogram (FECG)intrinsic mode functions (IMFs)non-invasive home fetal monitoringone-dimensional convolutional neural network (1D CNN)single-channel FECG extraction

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Fetal electrocardiogram (FECG) is vital for monitoring fetal well-being and detecting distress.
  • Abdominal electrocardiogram (AECG) offers home monitoring but suffers from weak signals and maternal interference.
  • Accurate selection of intrinsic mode functions (IMFs) is crucial for Ensemble Empirical Mode Decomposition (EEMD) analysis of AECG.

Purpose of the Study:

  • To develop an automated method for extracting FECG signals from AECG data.
  • To enhance the accuracy and reliability of FECG analysis for home-based fetal monitoring.
  • To improve the detection of fetal arrhythmias and distress using advanced signal processing and deep learning.

Main Methods:

  • A deep learning approach using a 1D convolutional neural network (1D CNN) to score and select EEMD-derived IMFs.
  • Maternal QRS complex template subtraction and secondary EEMD purification for automated FECG extraction.
  • Leave-one-subject-out (LOSO) cross-validation on simulated and real AECG datasets (ADFECGDB, DaISy, NIFEA).

Main Results:

  • The IMF classifier achieved a mean AUC of 0.9282 ± 0.0189.
  • The CNN-2×EEMD method demonstrated high correlation coefficients (0.94-0.96) and F1-scores (0.8372-0.9565) for fetal R-peak detection.
  • Significant SNR improvements (13.39-15.88 dB) were achieved, outperforming conventional methods and matching manual selection.

Conclusions:

  • The proposed deep learning-guided method enables accurate and automatic FECG extraction from AECG signals.
  • The technique shows significant potential for real-time, wearable fetal monitoring systems.
  • This advancement can improve the early detection of fetal cardiac issues and enhance prenatal care.