Abstract
A novel method for online analysis of the changes in signal modality is proposed. This is achieved by tracking the dynamics of the mixing parameter within a hybrid filter rather than the actual filter performance. An implementation of the proposed hybrid filter using a combination of the Least Mean Square (LMS) and the Generalised Normalised Gradient Descent (GNGD) algorithms is analysed and the potential of such a scheme for tracking signal nonlinearity is highlighted. Simulations on linear and nonlinear signals in a prediction configuration support the analysis. Biological applications of the approach have been illustrated on EEG data of epileptic patients. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Jelfs, B., Vayanos, P., Mo, C., Su, L. G., Boukis, C., Gautama, T., … Mandic, D. (2006). An online method for detecting nonlinearity within a signal. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4253 LNAI-III, pp. 1216–1223). Springer Verlag. https://doi.org/10.1007/11893011_154
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