ArticleSensors (Basel, Switzerland)2022
A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 12 citations in OpenAlex.
- Review of Non-Invasive Fetal Electrocardiography Monitoring Techniques.Sensors (Basel, Switzerland) · 2025Review
- [Fetal electrocardiogram signal extraction and analysis method combining fast independent component analysis algorithm and convolutional neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography.Frontiers in bioengineering and biotechnology · 2023Article
- Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval.Bioengineering (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-quality NI-FECG remains a challenge. Recording waveforms of sufficient quality for clinical use typically requires human visual inspection of each recording. A Signal Quality Index (SQI) can help to automate this task but, contrary to adult ECG, work on SQIs for NI-FECG is sparse. In this paper, a multi-channel signal quality classifier for NI-FECG waveforms is presented. The model can be used during the capture of NI-FECG to assist technicians to record high-quality waveforms, which is currently a labour-intensive task. A Convolutional Neural Network (CNN) is trained to distinguish between NI-FECG segments of high and low quality. NI-FECG recordings with one maternal channel and three abdominal channels were collected from 100 subjects during a routine hospital screening (102.6 min of data). The model achieves an average 10-fold cross-validated AUC of 0.95 ± 0.02. The results show that the model can reliably assess the FECG signal quality on our dataset. The proposed model can improve the automated capture and analysis of NI-FECG as well as reduce technician labour time.
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Registered trials
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