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|title: ||How to apply nonlinear subspace techniques to univariate biomedical time series|
|authors: ||Teixeira, A. R.|
Tomé, A. M.
Puntonet, Carlos G.
Lang, Elmar W.
|keywords: ||Kernel principal component analysis (KPCA)|
Local singular spectrum analysis (SSA)
|issue date: ||Aug-2009|
|publisher: ||Institute of Electrical and Electronics Engineers (IEEE)|
|abstract: ||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
|publisher version/DOI: ||http://dx.doi.org/10.1109/TIM.2009.2016385|
|source: ||IEEE Transactions on Instrumentation and Measurement|
|appears in collections||DETI - Artigos|
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