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Details of Grant 

EPSRC Reference: GR/N02641/01
Title: NONLINEAR DYNAMICAL METHODS FOR BIOMEDICAL SIGNAL PROCESSING
Principal Investigator: Tarassenko, Professor L
Other Investigators:
Smith, Professor LA
Researcher Co-Investigators:
Project Partners:
Department: Engineering Science
Organisation: University of Oxford
Scheme: Standard Research (Pre-FEC)
Starts: 06 March 2000 Ends: 05 March 2003 Value (£): 173,052
EPSRC Research Topic Classifications:
Digital Signal Processing
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
Recent work in the Neural Networks Research Group in Oxford University has sought to extend the techniques previously developed for sleep EEG analysis to more complex problems such as the assessment of vigilance and the detection of seizure onset in epilepsy. The assumption of stationarity which is inherent in all models so far used begins to break down before the onset of a seizure and during the trasitions from wakefulness to drowsiness. In the proposed research, the shortcomings of linear techniques for feature extraction when the EEG is undergoing rapid transitions motivate the investigation of nonlinear dynamical methods. The aim of applying such methods to the EEG and other biomedical signals is not to identify chaos, but rather to reconstruct a model state space in which the dynamics underlying the observations can be more conveniently analysed. The proposed research will provide an optimal state space reconstruction of EEG or ECG data, the evaluation of a number of candidate nonlinear indicators for changes in system states (including correlation dimension and short-term predictability), and a thorough analysis of the statistical significance of the results. In each case, the key issue of the patient specificity of the indicators will be examined in detail.
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Organisation Website: http://www.ox.ac.uk