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

• Published in: Journal of medical and biological engineering Vol. 44; no. 1; pp. 114 - 126
• Main Authors: Zhang, Yuwei, Gu, Aihua, Xiao, Zhijun, Ma, Caiyun, Wang, Zhongyu, Zhao, Lina, Yang, Chenxi, Li, Jianqing, Liu, Chengyu
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2024; Springer Nature B.V
• Subjects: Algorithms; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biomedicine; Classification; Classifiers; Data processing; EKG; Electrocardiography; Fetuses; Fractal geometry; Heart rate; Morbidity; Original Article; Performance indices; Quality assessment; Quality control; Regenerative Medicine/Tissue Engineering; Signal classification; Signal processing; Signal quality; Signal quality assessment; Fetal electrocardiography (fECG); Signal quality index; Fetal health monitoring; Classification
• ISSN: 1609-0985; 2199-4757
• DOI: 10.1007/s40846-024-00852-0

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.