This thesis presents a comprehensive work on computerised analysis of fetal heart rate (FHR) features, including feature extraction, feature selection, analysis of influencing factors and setting up/validation of a computerised decision support system. Firstly, a novel feature – pattern readjustment – was extracted and tested. Clinical data were used to train a Support Vector Machine (SVM) to detect pattern readjustment. Then, the association of pattern readjustment and adverse labour outcome was investigated. The validation results with clinical experts show that the pattern readjustment can be accurately detected, while the study on labour outcome shows that the feature is related to fetal acidemia at birth. Secondly, Genetic Algorithms were employed as a feature selection method to select a best subset of FHR features and to use them to predict fetal acidemia with linear and nonlinear SVM. The diagnostic power of the classifier output using selected features was tested on the total set of 7,568 cases. As the classifier output increases, there is a consistent increase of the risk of fetal acidemia. Thirdly, an important influencing factor on FHR features - signal loss – was investigated. A bivariate model was built to estimate error based on signal loss. Validation results show that the bivariate model can accurately predict the error generated by signal loss. The influence of signal loss on labour outcome classification was also investigated. Finally, a computerised decision support system to estimate the risk of fetal acidemia was set up based on the above studies. The system was validated using new retrospective data. Validation results show that the system is capable of predicting adverse labour outcome and providing timely decision support. It is the first time an intrapartum computerised FHR decision support system has been built and validated on this size of dataset. With further improvements, such a system could be implemented clinically in the long term.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:635298 |
| Date | January 2014 |
| Creators | Xu, Liang |
| Contributors | Georgieva, Antoniya; Payne, Stephen |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:9ad2cf2f-45aa-48df-b33f-da27087bd5da |
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