The use of a neural network to recognize placental insufficiency from blood flow velocity waveforms in the umbilical cord

Present-day obstetric decision-making is based on measuring the umbilical arterial blood flow velocity waveforms from one site of the cord. There is an ongoing debate on the predictive value of Doppler measurements in the evaluation of the foetal condition. The aim of this thesis is to investigate the use ofa neural network to recognise blood flow waveform shape patterns associated with placental insufficiency. Eleven backpropagation neural networks have been developed and trained based on the waveforms that are generated from the foetal mathematical model (developed in previous research) at both ends of the cord. Only two networks trained successfully. These two networks are the Levenberg-Marquardt algorithm (Trainlm) and the resilient backpropagation algorithm (Trainrp).

Grantor dc:publisher.institution

Division of Biomedical Engineering

Year dc:date.issued

2005

Author dc:creator

• Alhamud, Alkathafi Ali

Advisors dc:contributor.advisor

• Capper, Wayne
• Vaughan, Christopher Leonard (Kit)

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/3220

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/3220