Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.719853
Title: A perinatal monitoring system for low-resource settings
Author: Stroux, Lisa
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2015
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Abstract:
The reduction of maternal and child mortality has been central to international development efforts over the past twenty years, spurred on by global initiatives such as the Millennium Development Goals. Whereas a significant decrease in mortality rates has been achieved in mothers and the under-five, the fetal and early neonatal period has received less attention resulting in slower progress. Resource-poor settings suffer from the highest stillbirth, maternal and early neonatal death rates, with 99% occurring in low- and middle income countries. Causes of fetal and maternal compromise can be manifold, both infrastructural and pathophysiological. Common problems remain a scarcity of skilled healthcare personnel leading to low health risk detection, referral and ultimately intervention rates. In the absence of sophisticated equipment fetal compromise may go unnoticed, such as fetal growth restriction, one of the primary causes of poor health outcome. To work towards improved fetal and maternal risk assessment enabling timely and appropriate referral, this thesis proposes a low-cost mHealth monitoring platform for use in low-resource settings. The analysis of the fetal cardiac activity, prerequisite in hospitals in developed nations, may provide important insights on fetal stress levels and more specifically growth retardation. The fetal cardiac signal is accessible and obtained at low-cost with the help of a Doppler transducer. This thesis in healthcare innovation investigates the feasibility of introducing such technology in communities not familiar with their application, proposes a novel signal quality algorithm suitable for implementation on the phone, develops a classifier based on fetal heart rate variability to distinguish growth restricted from normal babies, and introduces the design of the mHealth platform, results of a pilot deployment in rural Guatemala and the design and implementation of a full randomised control trial.
Supervisor: Gifford, Gari D. ; Payne, Stephen Sponsor: RCUK Digital Economy Programme
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.719853  DOI: Not available
Share: