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Related Experiment Video

Updated: May 24, 2025

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TCGAN: Temporal Convolutional Generative Adversarial Network for Fetal ECG Extraction Using Single-Channel Abdominal

Zhen-Zhen Huang, Wei-Tao Zhang, Yang Li

    IEEE Journal of Biomedical and Health Informatics
    |March 3, 2025
    PubMed
    Summary
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    This study introduces a Temporal Convolutional Generative Adversarial Network (TCGAN) for extracting fetal ECG (FECG) from abdominal signals. TCGAN effectively isolates FECG, preserving waveform details for improved fetal development assessment.

    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Artificial Intelligence in Healthcare

    Background:

    • Noninvasive fetal electrocardiogram (FECG) monitoring is crucial for assessing fetal development.
    • Extracting FECG from abdominal ECG (AECG) is challenging due to maternal ECG (MECG) interference and noise.
    • Accurate FECG waveform details are essential for reliable fetal health assessment.

    Purpose of the Study:

    • To develop an advanced method for extracting FECG from single-channel AECG.
    • To improve the accuracy and detail preservation of FECG signals.
    • To enhance the capabilities of noninvasive fetal monitoring.

    Main Methods:

    • A Temporal Convolutional Generative Adversarial Network (TCGAN) was designed for FECG extraction.

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  • An encoder-decoder architecture with temporal convolution blocks, transpose convolutions, and skip connections was utilized.
  • The model was trained and validated using both synthetic (FECGSYDB) and real-world (ADFECGDB) datasets.

    • Main Results:

    • TCGAN demonstrated outstanding performance in fetal QRS complex detection, achieving high positive predictive values (PPV) of 99.54% and 99.02% on the datasets.
    • The method successfully extracted FECG signals with well-preserved waveform details.
    • Comparative analysis showed TCGAN outperforms state-of-the-art methods in FECG extraction accuracy.

    Conclusions:

    • TCGAN is a highly effective deep learning model for FECG extraction from single-channel AECG.
    • The preserved waveform details facilitate more accurate assessment of fetal development by clinicians.
    • This technology holds significant promise for advancing noninvasive fetal monitoring.