Search results for "Matrix decomposition"
showing 3 items of 43 documents
Spectral decomposition of RR-variability obtained by an open loop parametric model for the diagnosis of neuromediate syncope
2002
The role of the cardiovascular regulatory mechanism in patients with neuromediate syncope (NS) is poorly understood. The aim of this study was to accomplish continuous non-invasive analysis of the baroreflex mechanism in patients during a head-tip tilt-table test (HTT) using an open-loop autoregressive model with exogenous input. The model describes the causal dependence of the RR interval on the systolic arterial pressure (SAP) variability. Thus, RR variability results as the linear composition of SAP-dependent (Pdep) and SAP-independent parts of the RR power (P). Further, the model allows the estimation of the baroreflex gain using the modulus of the transfer function (G) from SAP to RR i…
New Types of Jacobian-Free Approximate Riemann Solvers for Hyperbolic Systems
2017
We present recent advances in PVM (Polynomial Viscosity Matrix) methods based on internal approximations to the absolute value function. These solvers only require a bound on the maximum wave speed, so no spectral decomposition is needed. Moreover, they can be written in Jacobian-free form, in which only evaluations of the physical flux are used. This is particularly interesting when considering systems with complex Jacobians, as the relativistic magnetohydrodynamics (RMHD) equations. The proposed solvers have also been extended to the case of approximate DOT (Dumbser-Osher-Toro) methods, which can be regarded as simple and efficient approximations to the classical Osher-Solomon method. Som…
Exploiting ongoing EEG with multilinear partial least squares during free-listening to music
2016
During real-world experiences, determining the stimulus-relevant brain activity is excitingly attractive and is very challenging, particularly in electroencephalography. Here, spectrograms of ongoing electroencephalogram (EEG) of one participant constructed a third-order tensor with three factors of time, frequency and space; and the stimulus data consisting of acoustical features derived from the naturalistic and continuous music formulated a matrix with two factors of time and the number of features. Thus, the multilinear partial least squares (PLS) conforming to the canonical polyadic (CP) model was performed on the tensor and the matrix for decomposing the ongoing EEG. Consequently, we …