Details of Grant 

EPSRC Reference:	GR/K55134/01
Title:	NEURAL COMPUTING FOR NONSTATIONARY MEDICAL SIGNAL PROCESSING
Principal Investigator:	Niranjan, Professor M
Other Investigators:	Prager, Professor R Trull, Dr A
Researcher Co-Investigators:
Project Partners:	Cambridge University Hospitals Trust
Department:	Engineering
Organisation:	University of Cambridge
Scheme:	Standard Research (Pre-FEC)
Starts:	01 October 1995	Ends:	31 August 1998	Value (£):	192,240
EPSRC Research Topic Classifications:
EPSRC Industrial Sector Classifications:	Healthcare No relevance to Underpinning Sectors
Related Grants:
Panel History:
Summary on Grant Application Form
This project addresses generic research issues and medical applications in the area of the key question: what is the optimum strategy when applying neural computing to time varying data? Two problems in the area of healthcare will be used as test beds for developing neural architectures and algorithms, that also may have application in other relayed areas such as time series analysis, signal processing and control of nonlinear systems. The healthcare applications involve monitoring the relationship between immuno-suppression drugs and liver function in transplant patients in order to develop more efficient dosage strategies and studying foetal heart rate signals to detect problems which could lead to asphyxia and hence cerebral palsy. The project will involve close collaboration between Cambridge University Engineering Department where the neural network research will be based. CU Department of Clinical Biochemistry responsible for the transplant application and CU Department of Obstetrics and Gynaecology working on the foetal heart rate application. The project will involve an initial phase of data collection and algorithm design related to the foetal heart rate and transplant problems. In the main phase of the project large scale experiments will be performed to develop neural network models which will then be evaluated towards the end of the project. By the end of the project we will have gained a significant insight into the efficient strategies for the processing of nonstationary signals and produced prototype solutions to the two medical problems.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Sectors submitted by the Researcher
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Further Information:
Organisation Website:	http://www.cam.ac.uk