Ana Maria Tomé

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In this work, we present a method to extract high-amplitude artefacts from single channel electroencephalogram (EEG) signals. The method is called local singular spectrum analysis (local SSA). It is based on a principal component analysis (PCA) applied to clusters of the multidimensional signals obtained after embedding the signals in their time-delayed(More)
Quinolones are a class of antibacterial agents for the treatment of several infectious diseases (e.g. urinary and respiratory tract infections). They are used worldwide due to their broad spectrum of activity, high bioavailability and good safety profile. The safety profile varies from quinolone to quinolone. The aim of this article was to review the(More)
MOTIVATION Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or(More)
In this paper we present denoising algorithms for enhancing noisy signals based on Local ICA (LICA), Delayed AMUSE (dAMUSE) and Kernel PCA (KPCA). The algorithm LICA relies on applying ICA locally to clusters of signals embedded in a high dimensional feature space of delayed coordinates. The components resembling the signals can be detected by various(More)
Multidimensional 1 H nmr spectra of biomolecules dissolved in light water are contaminated by an intense water artifact. Independent Component Analysis(ICA) is used to extract a set of signals out of a set of measured or sensed signals whithout knowing how the mixing process is carried out. Hence it is interesting to apply ICA techniques to the removal of(More)