Classification of fetal compromise during labour: signal processing and feature engineering of the cardiotocograph

Cardiotocography (CTG) is the main tool used for fetal monitoring during labour. Interpretation of CTG requires dynamic pattern recognition in real time. It is recognised as a difficult task with high inter- and intra-observer disagreement. Machine learning has provided a viable path towards objecti...

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Bibliographic Details
Published in2021 29th European Signal Processing Conference (EUSIPCO) pp. 1331 - 1335
Main Authors O'Sullivan, M., Gabruseva, T., Boylan, GB, O'Riordan, M., Lightbody, G., Marnane, W.
Format Conference Proceeding
LanguageEnglish
Published EURASIP 23.08.2021
Subjects
biomedical signal processing
cardiotocography
Control theory
Feature extraction
features engineering
Fetal heart rate
fetal monitoring
Machine learning
Signal processing
Time factors
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Summary:Cardiotocography (CTG) is the main tool used for fetal monitoring during labour. Interpretation of CTG requires dynamic pattern recognition in real time. It is recognised as a difficult task with high inter- and intra-observer disagreement. Machine learning has provided a viable path towards objective and reliable CTG assessment. In this study, novel CTG features are developed based on clinical expertise and system control theory using an autoregressive moving-average (ARMA) model to characterise the response of the fetal heart rate to contractions. The features are evaluated in a machine learning model to assess their efficacy in identifying fetal compromise. ARMA features ranked amongst the top features for detecting fetal compromise. Additionally, including clinical factors in the machine learning model and pruning data based on a signal quality measure improved the performance of the classifier.
ISSN:2076-1465
DOI:10.23919/EUSIPCO54536.2021.9616289