How to apply nonlinear subspace techniques to univariate biomedical tim e series

Teixeira, Ana; Tomé, A. M.; Böhm, M.; Puntonet, Carlos G.; Lang, Elmar W.

http://hdl.handle.net/10400.26/47383

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
How to apply nonlinear subspace techniques to univariate biomedical time series..pdf		402.58 KB	Adobe PDF	Download

Send Feedback

Authors

Abstract(s)

In this paper, we propose an embedding technique for univariate single-channel biomedical signals to apply projective subspace techniques. Biomedical signals are often recorded as 1-D time series; hence, they need to be transformed to multidimensional signal vectors for subspace techniques to be applicable. The transformation can be achieved by embedding an observed signal in its delayed coordinates. We propose the application of two nonlinear subspace techniques to embedded multidimensional signals and discuss their relation. The techniques consist of modified versions of singular-spectrum analysis (SSA) and kernel principal component analysis (KPCA). For illustrative purposes, both nonlinear subspace projection techniques are applied to an electroencephalogram (EEG) signal recorded in the frontal channel to extract its dominant electrooculogram (EOG) interference. Furthermore, to evaluate the performance of the algorithms, an experimental study with artificially mixed signals is presented and discussed.

Keywords

Electroencephalogram (EEG) electrooculogram (EOG) kernel principal component analysis (KPCA) local singular spectrum analysis (SSA) removing artifacts subspace techniques.