Advances in epileptic seizure onset prediction in the EEG with ICA and phase synchronization


Gupta, Disha (2009) Advances in epileptic seizure onset prediction in the EEG with ICA and phase synchronization University of Southampton, Institute of Sound and Vibration Research, Doctoral Thesis , 391pp.

Download

[img] PDF Gupta.pdf - Other
Download (60MB)

Description/Abstract

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.

Item Type: Thesis (Doctoral)
Subjects:

Organisations: University of Southampton
ePrint ID: 72166
Date :
Date Event
April 2009Published
Date Deposited: 01 Feb 2010
Last Modified: 18 Apr 2017 20:58
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/72166

Actions (login required)

View Item View Item