Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283352
Title: Intelligent fetal monitoring and decision support in the management of labour
Author: Keith, Robert Duncan Falconer
Awarding Body: University of Plymouth
Current Institution: University of Plymouth
Date of Award: 1993
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Abstract:
The condition of the fetus during labour is inferred from the continuous plot of fetal heart rate and uterine contractions (cardiotocogram, CTG). This can be _ difficult to interpret which results in both unnecessary intervention and a failure to intervene when necessary causing potentially preventable neurological damage and mortality. Conventional computing approaches have not been successful in addressing these problems. This is perhaps because the correct interpretation of fetal condition requires physiological knowledge, considerable practical experience and knowledge of the specific patient. The work described in this thesis is concerned with the investigation of artificial intelligence techniques to assist in the interpretation of fetal condition and advise on labour management. A fundamental investigation examined the performance of five types of scalp electrodes for obtaining the fetal electrocardiogram (ECG), from which heart rate is derived, and examined the factors which hamper fetal ECG data acquisition. New methods were developed to classify the important features from the CTG and included an investigation using neural networks. Other CTG features were classified using novel numerical algorithms developed closely with experts. An expert system, guided by a database of rules obtained from experts, was used to process and interpret changes in the CTG features by taking account of patient specific information. This hybrid approach was adopted to improve performance and reliability. After two internal evaluations had found the system obtained a performance comparable with local experts, an extensive external validation was undertaken. This study involved 17 experts from 16 leading centres within the UK. Each expert and the system reviewed 50 cases twice, at least one month apart which contained those considered most difficult to interpret selected from a database of 2400 high risk labours. A novel method was developed to present all the relevant clinical information in a way which approximated the clinical situation. The reviewers scored each 15 minutes of recording according to the concern they had for the fetus and the management they considered appropriate. In this respect, this is the first reported study to examine the performance of expert obstetricians in the management of labour. A new method was derived to measure the agreement between the scores obtained and is applicable to other areas where it is required to measure the similarity between time related sequences. This study found that the experts agreed well and were consistent in their management of the cases. The system was indistinguishable from the experts, except it was more consistent, even when used by an engineer with little knowledge of labour management. This study has shown that expertise in fetal monitoring is achievable in which case the current evidence suggests that this is not being adequately transferred to clinicians. The challenge remains to formulate a method to effectively transfer knowledge to the labour ward and thereby address the real and practical problems which face fetal monitoring today. This study demonstrates that intelligent systems could provide the vehicle to achieve this. I dedicate this work to the memory of my father, Bradley Kenneth Keith with a hope that he always believed it possible. I know he would have had some interesting comments to make and I sadly miss the opportunity of discussing them with him. I also dedicate this work to my mother for always being there, and to my wife Michelle for her unwavering support, patience and most of all her encouragement throughout this work.
Supervisor: Not available Sponsor: Science and Engineering Research Council ; Northcott Devon Medical Foundation ; Polytechnic Central Funding Council ; Mason Medical Research Foundation ; Royal Academy of Engineering
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.283352  DOI: Not available
Keywords: Childbirth; Expert systems; Biomedical data Biomedical engineering Biochemical engineering Bionics
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