The aim of the study was to assess sleep-wake habits and disorders and excessive daytime sleepiness (EDS) in an unselected outpatient epilepsy population. Sleep-wake habits and presence of sleep disorders were assessed by means of a clinical interview and a standard questionnaire in 100 consecutive patients with epilepsy and 90 controls. The questionnaire… (More)
A reduced order modeling method based on a system description in terms of orthonormal Laguerre functions, together with a Krylov subspace decomposition technique is presented. The link with Padé approximation, the block Arnoldi process and singular value decomposition (SVD) leads to a simple and stable implementation of the algorithm. Novel features of the… (More)
Clinical scores represent the gold standard in characterizing the clinical condition of patients in vegetative or minimally conscious state. However, they suffer from problems of sensitivity, specificity, subjectivity and inter-rater reliability. In this feasibility study, objective measures including physiological and neurophysiological signals are used to… (More)
A reduced order multiport modeling algorithm based on the decomposition of the system transfer matrix into orthogonal scaled Laguerre functions is proposed. The link with Padé approximation, the block Arnoldi method and singular value decomposition leads to a simple and stable implementation of the algorithm.
A provably stable reduced order model, based on a projection onto a scaled orthonormal La-guerre basis, followed by a SVD step, is proposed. The method relies on the conformal mapping properties induced by the complete orthonormal scaled Laguerre basis, allowing a mapping from the discrete-stable case to the continuous-stable case and vice versa.
A new model order reduction technique is presented which preserves passivity and non-expansivity. It is a projection-based method which exploits the solution of linear matrix inequalities to generate a descriptor state space format which preserves positive-realness and bounded-realness. In the case of both non-singular and singular systems, solving the… (More)
In statistical theory, the Huber function yields robust estimations reducing the effect of outliers. In this paper, we employ the Huber function as regularization in a challenging inverse problem: quantitative microwave imaging. Quantitative microwave tomography aims at estimating the permittivity profile of a scattering object based on measured scattered… (More)