4,294 research outputs found
Inference of stochastic nonlinear oscillators with applications to physiological problems
A new method of inferencing of coupled stochastic nonlinear oscillators is
described. The technique does not require extensive global optimization,
provides optimal compensation for noise-induced errors and is robust in a broad
range of dynamical models. We illustrate the main ideas of the technique by
inferencing a model of five globally and locally coupled noisy oscillators.
Specific modifications of the technique for inferencing hidden degrees of
freedom of coupled nonlinear oscillators is discussed in the context of
physiological applications.Comment: 11 pages, 10 figures, 2 tables Fluctuations and Noise 2004, SPIE
Conference, 25-28 May 2004 Gran Hotel Costa Meloneras Maspalomas, Gran
Canaria, Spai
Parametric uncertainty analysis of pulse wave propagation in a model of a human arterial network
Accepted versio
Critical comments on EEG sensor space dynamical connectivity analysis
Many different analysis techniques have been developed and applied to EEG
recordings that allow one to investigate how different brain areas interact.
One particular class of methods, based on the linear parametric representation
of multiple interacting time series, is widely used to study causal
connectivity in the brain. However, the results obtained by these methods
should be interpreted with great care. The goal of this paper is to show, both
theoretically and using simulations, that results obtained by applying causal
connectivity measures on the sensor (scalp) time series do not allow
interpretation in terms of interacting brain sources. This is because 1) the
channel locations cannot be seen as an approximation of a source's anatomical
location and 2) spurious connectivity can occur between sensors. Although many
measures of causal connectivity derived from EEG sensor time series are
affected by the latter, here we will focus on the well-known time domain index
of Granger causality (GC) and on the frequency domain directed transfer
function (DTF). Using the state-space framework and designing two simulation
studies we show that mixing effects caused by volume conduction can lead to
spurious connections, detected either by time domain GC or by DTF. Therefore,
GC/DTF causal connectivity measures should be computed at the source level, or
derived within analysis frameworks that model the effects of volume conduction.
Since mixing effects can also occur in the source space, it is advised to
combine source space analysis with connectivity measures that are robust to
mixing
Cardiac cell modelling: Observations from the heart of the cardiac physiome project
In this manuscript we review the state of cardiac cell modelling in the context of international initiatives such as the IUPS Physiome and Virtual Physiological Human Projects, which aim to integrate computational models across scales and physics. In particular we focus on the relationship between experimental data and model parameterisation across a range of model types and cellular physiological systems. Finally, in the context of parameter identification and model reuse within the Cardiac Physiome, we suggest some future priority areas for this field
Mathematical Modelling of Heart Rate Changes in the Mouse
The CVS is composed of numerous interacting and dynamically regulated physiological subsystems which each generate measurable periodic components such that the CVS can itself be presented as a system of weakly coupled oscillators. The interactions between these oscillators generate a chaotic blood pressure waveform signal, where periods of apparent rhythmicity are punctuated by asynchronous behaviour. It is this variability which seems to characterise the normal state. We used a standard experimental data set for the purposes of analysis and modelling. Arterial blood pressure waveform data was collected from conscious mice instrumented with radiotelemetry devices over hours, at a Hz and kHz time base. During a hour period, these mice display diurnal variation leading to changes in the cardiovascular waveform. We undertook preliminary analysis of our data using Fourier transforms and subsequently applied a series of both linear and nonlinear mathematical approaches in parallel. We provide a minimalistic linear and nonlinear coupled oscillator model and employed spectral and Hilbert analysis as well as a phase plane analysis. This provides a route to a three way synergistic investigation of the original blood pressure data by a combination of physiological experiments, data analysis viz. Fourier and Hilbert transforms and attractor reconstructions, and numerical solutions of linear and nonlinear coupled oscillator models. We believe that a minimal model of coupled oscillator models that quantitatively describes the complex physiological data could be developed via such a method. Further investigations of each of these techniques will be explored in separate publications
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