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An Integrated Framework for Assessing the Quality of Non-invasive Fetal Electrocardiography Signals

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Abstract

Purpose

Non-invasive fetal electrocardiography (fECG) offers crucial information for assessing early diagnosis of fetal distress and morbidity. However, the non-invasive fECG signals probably contain various non-stationary noises, which may generate a bad influence on signal processing. Signal quality assessment plays a crucial role in accurate feature estimation for obtaining high-quality signals.

Methods

This study develops a comprehensive framework for the assessment of signal quality for non-invasive fECG signals. Firstly, the ECG collection equipment is employed to gather abdominal ECG signal data from eight pregnant women in the hospital. Secondly, signal preprocessing is operated including signal segmentation and data normalization process. Subsequently, a total of thirty-seven signal quality indexes (SQIs) are extracted which consist of the amplitude-based SQI, R-wave-based SQI, statistical-based SQI, fractal dimension SQI, power spectrum distribution-based SQI, and entropy domain-based SQI. Then, in order to reduce the dimensionality of features and improve the experimental performance, information gain is carried out to identify the subset of the optimal features. At last, the classifier combines different feature numbers to classify the quality of the non-invasive fECG signal.

Results

Ten classifiers are selected to perform a classification task between good-quality and bad-quality abdominal signals. The experimental results show that the combination of twenty-four effective features and random forest achieved the highest classification outcome, which in terms of the ACC, and F1 scores are 0.9508, and 0.9510, respectively.

Conclusion

The experimental results indicate that our work can reliably assess the signal quality for non-invasive fECG signals and filter out good-quality signals. This proposed algorithm can help to improve the accuracy of fetal signal extraction and fetal heart rate estimation for further analysis, which is beneficial to promoting fetal health monitoring.

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Data Availability

Not applicable.

Code Availability

https://github.com/Lucky0923/fetal-SQA.

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Acknowledgements

The authors appreciate the support from the Southeast–Lenovo Wearable Heart-Sleep-Emotion Intelligent Monitoring Lab.

Funding

This research was funded by the National Natural Science Foundation of China (62171123, 62071241, 62101120, 62201144, and 62211530112), the Natural Science Foundation of Jiangsu Province (BK20210208, BE2022160), the Young Elite Scientists Sponsorship Program by CAST (2022QNRC001), and Ministry of Education’s Chunhui Jihua Cooperative Research Project (HZKY20220155).

Author information

Authors and Affiliations

  1. School of Instrument Science and Engineering, Southeast University, Nanjing, China

    Yuwei Zhang, Zhijun Xiao, Caiyun Ma, Zhongyu Wang, Lina Zhao, Chenxi Yang, Jianqing Li & Chengyu Liu

  2. School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China

    Yuwei Zhang & Aihua Gu

  3. School of Public Health, Nanjing Medical University, Nanjing, 211166, China

    Aihua Gu

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  1. Yuwei Zhang
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Contributions

Yuwei Zhang, Aihua Gu, and Chengyu Liu designed the experiment. Yuwei Zhang, Zhijun Xiao, Caiyun Ma, and Zhongyu Wang conducted the experiments. Yuwei Zhang and Chengyu Liu drafed the paper. Chenxi Yang, Lina Zhao, and Jianqing Li refined the manuscript. Lina Zhao, Jianqing Li, and Chengyu Liu provided the experiment equipment and project support. All the authors contributed to the result presentation and discussion.

Corresponding authors

Correspondence to Aihua Gu or Chengyu Liu.

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Author Contributions

Yuwei Zhang, Aihua Gu, and Chengyu Liu designed the experiment. Yuwei Zhang, Zhijun Xiao, Caiyun Ma, and Zhongyu Wang conducted the experiments. Yuwei Zhang and Chengyu Liu drafted the paper. Chenxi Yang, Lina Zhao, and Jianqing Li refined the manuscript. Lina Zhao, Jianqing Li, and Chengyu Liu provided the experiment equipment and project support. All the authors contributed to the result presentation and discussion.

Competing interests

The authors declare no conflict of interest.

Ethical Approval

The protocol of this study was approved by the Ethics Committee of the Jiangsu Provincial People’s Hospital.

Consent to Participate

Written informed consent was given by all participants in accordance with the Declaration of Helsinki.

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Zhang, Y., Gu, A., Xiao, Z. et al. An Integrated Framework for Assessing the Quality of Non-invasive Fetal Electrocardiography Signals. J. Med. Biol. Eng. 44, 114–126 (2024). https://doi.org/10.1007/s40846-024-00852-0

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