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A Novel Approach to Fetal ECG Extraction Using Temporal Convolutional Encoder–Decoder Network (TCED-Net)

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Abstract

To extract weak fetal ECG signals from the mixed ECG signal on the mother's abdominal wall, providing a basis for accurately estimating fetal heart rate and analyzing fetal ECG morphology. First, based on the relationship between the maternal chest ECG signal and the maternal ECG component in the abdominal signal, the temporal convolutional encoder-decoder network (TCED-Net) model is trained to fit the nonlinear transmission of the maternal ECG signal from the chest to the abdominal wall. Then, the maternal chest ECG signal is nonlinearly transformed to estimate the maternal ECG component in the abdominal mixed signal. Finally, the estimated maternal ECG component is subtracted from the abdominal mixed signal to obtain the fetal ECG component. The simulation results on the FECGSYN dataset show that the proposed approach achieves the best performance in F1 score, mean square error (MSE), and quality signal-to-noise ratio (qSNR) (98.94%, 0.18, and 8.30, respectively). On the NI-FECG dataset, although the fetal ECG component is small in energy in the mixed signal, this method can effectively suppress the maternal ECG component and thus extract a clearer and more accurate fetal ECG signal. Compared with existing algorithms, the proposed method can extract clearer fetal ECG signals, which has significant application value for effective fetal health monitoring during pregnancy.

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All data, models, and code generated or used during the study appear in the submitted article.

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Acknowledgements

We would like to express our gratitude to Dr. Yisheng Wu and Dr. Shangping Chen for their valuable insights and suggestions during the development of this research project. We also thank the staff at Zhaoqing First People's Hospital for their assistance with data collection and analysis.

Funding

This work was supported in part by the Medical Science and Technology Research Fund of Guangdong Province (B2020199, B2023392), Guangdong Province Continuing Education Quality Improvement Project (JXJYGC2022GX543), Characteristic Innovation Projects of Ordinary University in Guangdong Province (2019GKTSCX129), Guangdong Vocational College Teaching Management Steering Committee Education and Teaching Reform Key Project (YJXGLW2022Z10), Guangdong Higher Education Association “14th Five-Year” Plan 2023 Higher Education Research Key Project (23GZD22), Scientific research planning project of Guangdong Vocational and Technical Education Association (202212G247), and The Medical Simulation Education Teaching Research Project of Guangdong Provincial Higher Vocational Education Medical and Health Professional Teaching Steering Committee (2022MYLX081).

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Authors and Affiliations

  1. Zhaoqing Medical College, Zhaoqing, 526000, Guangdong, China

    Haiping Huang

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  1. Haiping Huang
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HH completed this study on her own.

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Correspondence to Haiping Huang.

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Ethical Approval

This study involving human participants, human material, or human data, was performed in accordance with the Declaration of Helsinki. The experimental protocols were approved by the Institutional Review Board (IRB) of Zhaoqing Medical College (Approval No. 2022-0124).

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Informed consent was obtained from all participants prior to their inclusion in the study.

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Informed consent for the publication of identifying information and images,which could lead to the identification of study participants, was obtained from all subjects. All participants agreed to the publication of their anonymized data in this online open-access publication.

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Huang, H. A Novel Approach to Fetal ECG Extraction Using Temporal Convolutional Encoder–Decoder Network (TCED-Net). Pediatr Cardiol 44, 1726–1735 (2023). https://doi.org/10.1007/s00246-023-03273-z

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  • DOI: https://doi.org/10.1007/s00246-023-03273-z

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