This Thesis compares CTG classification techniques proposed in the literature and their potential extensions. A comparison between four classifiers previously assessed - Adaboost, Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machine (SVM) - and two proposed classifiers - Bayesian ANN (BANN), Relevance Vector Machine - was conducted using a database of 7,568 cases and two open source databases. The Random Forest (RF) achieved the highest average result and was proposed as a benchmark classifier. The proposal to use model certainty to introduce a third, unclassified, class was investigated using the BANN. An increase in the classification accuracy was demonstrated, however the proportion of cases in the unclassified class was too great to be of practical value. The information content of time series was explored using a Hidden Markov Model (HMM). The average performance of the HMM was comparable with the performance of the benchmark with a smaller distribution across validation folds, demonstrating that time-series information provides more stable estimates of class than stationary methods. Finally a method of system identification was implemented. Significant differences between feature trends and histograms in low pH (< 7.1) and healthy pH (≥ 7.1) cases were observed. These features were used as classifier inputs, and achieved performance similar to existing feature sets. When these features were aligned according the onset of stage 2 labour three unique trend patterns were discovered.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:729976 |
| Date | January 2016 |
| Creators | Clibbon, Alex P. |
| Contributors | Payne, Stephen |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://ora.ox.ac.uk/objects/uuid:550bb5ea-bee8-4eb8-95e2-f16c54d7cd68 |
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