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Features are extracted from PET images employing exploratory matrix factorization techniques, here non-negative matrix factorization (NMF). Appropriate features are fed into classifiers such as support vector machine or random forest. An automatic classification is achieved with high classification rate and only few false negatives.
Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes...
Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. The method is fully adaptive and generates the basis to represent...
Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Intelligent signal processing is crucial to unravel the information content buried in biomedical time series. Empirical Mode Decomposition is ideally suited to extract all pure oscillatory modes which are contained in the signal. These modes, called Intrinsic Mode Functions (IMFs), represent a complete set of...
A novel network based on Linsker-type Hebbian learning is analyzed in its dynamical behavior. The network combines a coupled dynamics of fast and slow states and is prone to internal parametrical fluctuations as well as external noises. Robustness represents a crucial property of the network to attenuate the effects of internal fluctuation and external noise. In this study, we formulate this novel...
Features are extracted from PET images employing exploratory matrix factorization techniques such as nonnegative matrix factorization (NMF). Appropriate features are fed into classifiers such as a support vector machine or a random forest tree classifier. An automatic feature extraction and classification is achieved with high classification rate which is robust and reliable and can help in an early...
We use two spatiotemporal Independent Component Analysis algorithms, stJADE and stSOBI, to analyse data from a retinotopic functional magnetic resonance imaging experiment and compare their performance to the analysis of the same data with the spatial ICA done with JADE. This kind of experimental setting has the advantage that the activation in the brain can be estimated fairly easily and therefore...
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