PREDICTION OF EPILEPSY SEIZURE ONSET USING NONLINEAR ANALYSIS OF EEG RECORDINGS
Grant number: LP0560684 | Funding period: 2006 - 2010
This project will develop the theory and algorithms for reliable and robust prediction of the onset of epileptic seizures and the characterisation of epileptic seizures based on EEG data. Our interdisciplinary team consists of neuroscientists and systems engineers supported with clinicians and software developers. The team will develop the theory and design, implement and evaluate decision support software that is able to interpret eeg data and present epilepsy relevant information to clinicians and patients. Our methods are based on statistical signal processing, nonlinear dynamics (bifurcation and time-series methods) and systems engineering (system identification, adaptive methods).
Related publications (6)
Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons
Levin Kuhlmann, Dean Freestone, Alan Lai, Anthony N Burkitt, Karen Fuller, David B Grayden, Linda Seiderer, Simon Vogrin, Iven MY Mareels, Mark J Cook
This paper evaluates the patient-specific seizure prediction performance of pre-ictal changes in bivariate-synchrony between pairs..
Seizure Detection Using Seizure Probability Estimation: Comparison of Features Used to Detect Seizures
Levin Kuhlmann, Anthony N Burkitt, Mark J Cook, Karen Fuller, David B Grayden, Linda Seiderer, Iven MY Mareels
This paper analyses seizure detection features and their combinations using a probability-based scalp EEG seizure detection framew..