This thesis applies several standard nonlinear quantifiers to EEG analysis to examine both human primary generalised epilepsy (PGE) and rat models of human epilepsy.
We analysed rat EEG, and then used the analysed data, in parallel with an impedance recording, to better understand the events during experiments.
Next, the nonlinear analysis of EEG was used to attempt to model the behaviour of the impedance data. This modeling did not yield a useful predictive tool, so we recommend the continued recording of impedance data as a means of augmenting EEG recordings.
The analyses were also applied to human data, and showed differences between the PGE and control groups in apparently normal EEG. We then attempted to use these differences to detect the presence of PGE in an unclassified subject a diagnostic tool. This was done using a feed-forward neural network. We found that the inter-group differences were exploitable and facilitated the diagnosis of PGE in previously unknown subjects. The extent to which this is useful as a diagnostic tool should be assessed by further trials.
Finally, the analyses were used to examine data from a paralysed human subject, in an attempt to identify the mental task being performed by that
subject. This was not successful, suggesting that the same analyses that were useful in discriminating between PGE and control were not useful in detecting the mental state of the subject. It was also apparent that the presence of EMG (in an unparalysed state) assisted task-classification.
| Identifer | oai:union.ndltd.org:ADTP/207955 |
| Date | January 2008 |
| Creators | Wallace, Angus Keith, wallace.angus@gmail.com |
| Publisher | Flinders University. Computer Science, Engineering and Mathematics |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://www.flinders.edu.au/disclaimer/), Copyright Angus Keith Wallace |
Page generated in 0.0017 seconds