Finite rank series modeling for discrimination of non-stationary signals

Abstract

The analysis of time-variant biosignals for classification tasks, usually requires a modeling that may handel their different dynamics and non-stationary components. Although determination of proper stationary data length and the model parameters remains as an open issue. In this work, time-variant signal decomposition through Finite Rank Series Modeling is carried out, aiming to find the model parameters. Three schemes are tested for OSA detection based on HRV recordings: SSA and DLM as linear decompositions and EDS as non-linear decomposition. Results show that EDS decomposition presents the best performance, followed by SSA. As a conclusion, it can be inferred that adding complexity at the linear model the trend is approximate to a simple non-linear model. © 2012 Springer-Verlag.