Recent Advances in Biomedical Signal Processing

Indexed in: Scopus, EBSCO.

Biomedical signal processing is a rapidly expanding field with a wide range of applications, from the construction of artificial limbs and aids for disabilities to the development of sophisticated ...
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Subspace Techniques and Biomedical Time Series Analysis

Pp. 48-59 (12)

A. M. Tome, A. R. Teixeira and E. W. Lang

Abstract

The application of subspace techniques to univariate (single-sensor) biomedical time series is presented. Both linear and non-linear methods are described using algebraic models, and the dot product is the most important operation concerning data manipulations. The covariance/ correlationmatrices, computed in the space of time-delayed coordinates or in a feature space created by a non-linear mapping, are employed to deduce orthogonal models. Linear methods encompass singular spectrum analysis (SSA), singular value decomposition (SVD) or principal component analysis (PCA). Local SSA is a variant of SSA which can approximate non-linear trajectories of the embedded signal by introducing a clustering step. Generically non-linear methods encompass kernel principal component analysis (KPCA) and greedy KPCA. The latter is a variant where the subspace model is based on a selected subset of data only

Keywords:

Kernel methods, projective subspace techniques, time series analysis.

Affiliation:

IEETA/DETI, Universidade de Aveiro, P-3810-193 Aveiro, Portugal