University of Lisbon

About: University of Lisbon is a education organization based out in Lisbon, Lisboa, Portugal. It is known for research contribution in the topics: Population & Context (language use). The organization has 19122 authors who have published 48503 publications receiving 1102623 citations. The organization is also known as: Universidade de Lisboa & Lisbon University.

Multiscale entropy analysis of complex physiologic time series.

Abstract: There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.

Clinical diagnostic criteria for dementia associated with Parkinson's disease.

Abstract: Dementia has been increasingly more recognized to be a common feature in patients with Parkinson's disease (PD), especially in old age. Specific criteria for the clinical diagnosis of dementia associated with PD (PD-D), however, have been lacking. A Task Force, organized by the Movement Disorder Study, was charged with the development of clinical diagnostic criteria for PD-D. The Task Force members were assigned to sub-committees and performed a systematic review of the literature, based on pre-defined selection criteria, in order to identify the epidemiological, clinical, auxillary, and pathological features of PD-D. Clinical diagnostic criteria were then developed based on these findings and group consensus. The incidence of dementia in PD is increased up to six times, point-prevelance is close to 30%, older age and akinetic-rigid form are associated with higher risk. PD-D is characterized by impairment in attention, memory, executive and visuo-spatial functions, behavioral symptoms such as affective changes, hallucinations, and apathy are frequent. There are no specific ancillary investigations for the diagnosis; the main pathological correlate is Lewy body-type degeneration in cerebral cortex and limbic structures. Based on the characteristic features associated with this condition, clinical diagnostic criteria for probable and possible PD-D are proposed.

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Abstract: Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.