Epilepsy research using nonlinear signal processing

Author: Angus Keith Wallace

Wallace, Angus Keith, 2008 Epilepsy research using nonlinear signal processing, Flinders University, School of Computer Science, Engineering and Mathematics

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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.

Keywords: epilepsy,nonlinear,signal processing,classification,neuroscience,model

Subject: Biomedical Engineering thesis

Thesis type: Doctor of Philosophy
Completed: 2008
School: School of Computer Science, Engineering and Mathematics
Supervisor: Dr Kenneth Pope