Use this URL to cite or link to this record in EThOS:
Title: Advances in epileptic seizure onset prediction in the EEG with ICA and phase synchronization
Author: Gupta, Disha
ISNI:       0000 0004 2677 3327
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2009
Availability of Full Text:
Access from EThOS:
Access from Institution:
Seizure onset prediction in epilepsy is a challenge which is under investigation using many and varied signal processing techniques, across the world. This research thesis contributes to the advancement of digital signal analysis of neurophysiological signals of epileptic patients. It has been studied especially in the context of epileptic seizure onset prediction, with a motivation to help epileptic patients by advancing the knowledge on the possibilities of seizure prediction and inching towards a clinically viable seizure predictor. In this work, a synchrony based multi-stage system is analyzed that brings to bear the advantages of many techniques in each substage. The 1st stage of the system unmixes and de-noises continuous long-term (2-4 days) multichannel scalp Electroencephalograms using spatially constrained Independent Component Analysis. The 2d stage estimates the long term significant phase synchrony dynamics of narrowband (2-8 Hz and 8-14 Hz) seizure components. The synchrony dynamics are assessed with a novel statistic, the PLV-d, analyzing the joint synchrony in two frequency bands of interest. The 3rd stage creates multidimensional features of these synchrony dynamics for two classes (‘seizure free’ and ‘seizure predictive’) which are then projected onto a 2-dimensional map using a supervised Neuroscale, a topographic projection scheme based on a Radial Basis Neural Network. The 4th stage evaluates the probability of occurrence of predictive events using Gaussian Mixture Models used in supervised and semi-supervised forms. Preliminary analysis is performed on shorter data segments and the final system is based on nine patient’s long term (2-4 days each) continuous data. The training and testing for feature extraction analysis is performed on five patient datasets. The features extracted and the parameters ascertained with this analysis are then applied on the remaining four long-term datasets as a test of performance. The analysis is tested against random predictors as well. We show the possibility of seizure onset prediction (performing better than a random predictor) within a prediction window of 35-65 minutes with a sensitivity of 65-100% and specificity of 60-100% across the epileptic patients.
Supervisor: James, Christopher Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: RZ Other systems of medicine ; RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry ; TA Engineering (General). Civil engineering (General)