Abstract
Computerised detection and prediction of epileptic discharges from EEG data is a problem whose solution may lead to the prediction of epileptic seizures and planning of treatment. The recently confirmed fact that the EEG has a fractal nature enables a new approach to analysis of epilepsy. A conventional signal processing approach is not appropriate for highly complex signals, such as chaotic deterministic signals including epileptiform EEG. In our previous work we found that the graph dimension is the most appropriate measure for real-time fractal dimension estimation of EEG signals, and that it could be used for differentiation between parts of EEG signals with and without epileptic discharges. However, we also found that this measure is especially sensitive to signal artefacts and signal noise. In this paper we present the results of our search for a more robust measure for differentiation. The mean singular value is our new quantitative measure which in combination with the graph dimension can be used for differentiation of different EEG states (with and without epileptic discharges).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
