Algorithm for the Detection of Changes in the Dynamics of a Multivariate Time Series via Sliced Cross-Bispectrum.

In this paper, we propose a novel algorithm for the detection changes in the spatio-spectral dynamics of a multivariate time series based on a sliced cross-bispectrum. The singular value decomposition is performed on the matrix of the sliced cross-bispectrum at every frequency of the bandwidth, and a 2D spectrum of the eigenvalues is obtained. The changes in the dynamics are translated into differences between the reference and current values of the 2D eigenvalue spectrum. Hellinger divergence as the measure of the distance between eigenvalue spectrum matrices is used. The performance of the proposed approach is tested on model data and applied to multi-channel electroencephalogram recordings from epilepsy patients to detect ictal states. [ABSTRACT FROM AUTHOR]