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

• Main Authors: Clifton, D.A (Author), Li, Y. (Author), Liu, Z. (Author), Long, Y. (Author), Mertes, G. (Author), Yang, Y. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Biomedical signal processing; Convolution; convolutional neural network; Convolutional neural network; Convolutional neural networks; Deep learning; Electrocardiography; Fetal ECG; Fetal electrocardiographies; foetal ECG; High quality; Hospitals; Learning approach; Monitoring methods; Noise source; Nonstationary noise; signal quality; Signal quality; Waveforms
• Online Access: View Fulltext in Publisher

 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.