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A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography
- Source :
- Sensors; Volume 22; Issue 9; Pages: 3303
- Publication Year :
- 2022
- Publisher :
- MDPI AG, 2022.
-
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 (102.6amp;nbsp;min of data). The model achieves an average 10-fold cross-validated AUC of 0.95amp;nbsp;±amp;nbsp;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.
- Subjects :
- foetal ECG
convolutional neural network
signal quality
Signal Processing, Computer-Assisted
Biochemistry
Atomic and Molecular Physics, and Optics
Analytical Chemistry
Electrocardiography
Deep Learning
Fetus
Pregnancy
Humans
Female
Neural Networks, Computer
Electrical and Electronic Engineering
Instrumentation
Algorithms
Subjects
Details
- ISSN :
- 14248220
- Volume :
- 22
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....396572a98b581662038b6b5ceaef2a3f