Novel Tensor-based Singular Spectrum Decomposition

Tensor-based signal decomposition methods offer a promising avenue for signal decomposition of short, non-stationary and non-linear input signals. A novel Tensor-based Singular Spectrum Decomposition (TSSD) framework is presented that extends Singular Spectrum Decomposition (SSD) to tensors for univariate signals using two tensor decomposition techniques, namely, Multilinear Singular Value Decomposition (MLSVD) and Canonical Polyadic Decomposition (CPD). Results indicate improved performance under the influence of noise and in the presence of sizeable trends. Experiments on real-life data on EEG signals from epileptic seizures further show the strong practical relevance of TSSD as a tool for exploratory signal analysis that helps unveil underlying system(s) in signals.