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. 2022 Apr 26;22(9):3303.
doi: 10.3390/s22093303.

A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography

Gert Mertes  1   2 , Yuan Long  3 , Zhangdaihong Liu  1   2 , Yuhui Li  4 , Yang Yang  1   2 , David A Clifton  1   2
Affiliations

A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography

Gert Mertes et al. Sensors (Basel). .

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.

Keywords: convolutional neural network; foetal ECG; signal quality.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure A1
Figure A1
Confusion matrix of the CNN-FSQI cross-validation results. It shows the total number of predictions summed over all repetitions of the outer 10-fold CV.
Figure A2
Figure A2
Examples of good- and bad-quality segments. Blue circle: maternal R peak, red cross: foetal R peak.
Figure A3
Figure A3
The full neural network architecture.
Figure 1
Figure 1
Example of NI-FECG. The abdominal mixture (a) contains both a maternal (b) and a foetal (c) component. The foetal component has a lower SNR than the maternal component. The figures depict an ideal case. In practice, the SNR is typically worse.
Figure 2
Figure 2
The NI-FECG electrode configuration used in this work.
Figure 3
Figure 3
Examples of good- and bad-quality segments. Only the first abdominal channel is shown. Blue circle: maternal R peak, red cross: foetal R peak.
Figure 4
Figure 4
Time–frequency representation of an abdominal ECG segment (single-channel) with a good ground truth quality label.
Figure 5
Figure 5
Neural network architecture for NI-FECG signal quality prediction.
Figure 6
Figure 6
Detailed architecture of the CNN path for one channel. The network contains four of these in parallel.
Figure 7
Figure 7
Illustration of the nested cross-validation procedure.
Figure 8
Figure 8
Examples of true and false predictions. Only the first abdominal channel is shown. The ground truth foetal R peaks are labelled with a red cross.
Figure 9
Figure 9
Training graphs for the best (a) and worst (b) repetitions of the CV procedure.
See this image and copyright information in PMC

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