Sparsity in the multivariate wavelet framework:A comparative study using epileptic electroencephalography data

Gibberd, A. J. and Nelson, J. (2015) Sparsity in the multivariate wavelet framework:A comparative study using epileptic electroencephalography data. In: 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP). UNSPECIFIED.

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Abstract

We consider how recently developed multi-resolution exploratory graphical models (MR-EGM) may be estimated in a practical real-world situation. A simple cross-validation procedure based on minimising predictive risk is presented as a means to estimate tuning parameters. Through the use of electroencephalography (EEG) data, we attempt to use such a procedure to build a generative (multi-resolution) model of the electrical dynamics in the brain throughout an epileptic seizure. Brain dynamics are analysed by projecting the estimated model parameters onto their principle components where we identify two clusters of seizure activity. To conclude, we discuss the interpretation of such a principle component analysis and how well we can generalise between seizures on a specific patient.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
128569
Deposited By:
Deposited On:
06 Nov 2018 14:48
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Jan 2020 10:52