Search results for " Informatica"
showing 10 items of 978 documents
Pre- and post-ictal brain activity characterization using combined source decomposition and connectivity estimation in epileptic children
2019
In this research, the study of functional connectivity between sources of electroencephalogram (EEG) activity assessed for different classes (well before seizure, preictal and post-ictal) was performed. EEG recordings were acquired from 12 subjects with focal epilepsy. Then, ten common spatial patterns (CSP) were obtained for EEG segments describing 95% of Riemannian distance between pairs of classes, followed by estimation of multivariate autoregressive (MVAR) models’ coefficients. The MVAR models were further used to extract coherence as a functional connectivity measures. Our results show that the coherence between CSP sources differs between baseline and pre-ictal segments: it has the l…
Multivariate Frequency Domain Analysis of Causal Interactions in Physiological Time Series
2011
A common way of obtaining information about a physiological system is to measure one or more signals from the system, consider their temporal evolution in the form of numerical time series, and obtain quantitative indexes through the application of time series analysis techniques. While historical approaches to time series analysis were addressed to the study of single signals, recent advances have made it possible to study collectively the behavior of several signals measured simultaneously from the considered system. In fact, multivariate (MV) time series analysis is nowadays extensively used to characterize interdependencies among multiple signals collected from dynamical physiological s…
Testing different methodologies for Granger causality estimation: A simulation study
2021
Granger causality (GC) is a method for determining whether and how two time series exert causal influences one over the other. As it is easy to implement through vector autoregressive (VAR) models and can be generalized to the multivariate case, GC has spread in many different areas of research such as neuroscience and network physiology. In its basic formulation, the computation of GC involves two different regressions, taking respectively into account the whole past history of the investigated multivariate time series (full model) and the past of all time series except the putatively causal time series (restricted model). However, the restricted model cannot be represented through a finit…
Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series
2020
Cardiovascular variability is the result of the activity of several physiological control mechanisms, which involve different variables and operate across multiple time scales encompassing short term dynamics and long range correlations. This study presents a new approach to assess the multiscale complexity of multivariate time series, based on linear parametric models incorporating autoregressive coefficients and fractional integration. The approach extends to the multivariate case recent works introducing a linear parametric representation of multiscale entropy, and is exploited to assess the complexity of cardiovascular and respiratory time series in healthy subjects studied during postu…
Knots, Music and DNA
2020
Musical gestures connect the symbolic layer of the score to the physical layer of sound. I focus here on the mathematical theory of musical gestures, and I propose its generalization to include braids and knots. In this way, it is possible to extend the formalism to cover more case studies, especially regarding conducting gestures. Moreover, recent developments involving comparisons and similarities between gestures of orchestral musicians can be contextualized in the frame of braided monoidal categories. Because knots and braids can be applied to both music and biology (they apply to knotted proteins, for example), I end the article with a new musical rendition of DNA.
Complexity of operations on cofinite languages
2010
International audience; We study the worst case complexity of regular operation on cofinite languages (i.e., languages whose complement is finite) and provide algorithms to compute efficiently the resulting minimal automata.
Model-Based Transfer Entropy Analysis of Brain-Body Interactions with Penalized regression techniques
2020
The human body can be seen as a functional network depicting the dynamical interactions between different organ systems. This exchange of information is often evaluated with information-theoretic approaches which comprise the use of vector autoregressive (VAR) and state space (SS) models, normally identified with the Ordinary Least Squares (OLS). However, the number of time series to be included in the model is strictly related to the length of data recorded thus limiting the use of the classical approach. In this work, a new method based on penalized regressions, the so-called LASSO, was compared with OLS on physiological time-series extracted from 18 subjects during different stress condi…
On the cellular mechanisms underlying working memory capacity in humans
2016
The cellular processes underlying individual differences in the Working Memory Capacity (WMC) of humans are essentially unknown. Psychological experiments suggest that subjects with lower working memory capacity (LWMC), with respect to subjects with higher capacity (HWMC), take more time to recall items from a list because they search through a larger set of items and are much more susceptible to interference during retrieval. However, a more precise link between psychological experiments and cellular properties is lacking and very difficult to investigate experimentally. In this paper, we investigate the possible underlying mechanisms at the single neuron level by using a computational mod…
Propagation pattern analysis during atrial fibrillation based on sparse modeling.
2012
In this study, sparse modeling is introduced for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence function, derived from fitting a multivariate autoregressive model to the observed signal using least-squares (LS) estimation. The propagation pattern analysis incorporates prior information on sparse coupling as well as the distance between the recording sites. Two optimization methods are employed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO), and a novel method named the distance-adaptive group LASSO (dLASSO). Using si…
Propagation pattern analysis during atrial fibrillation based on the adaptive group LASSO.
2012
The present study introduces sparse modeling for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence (PDC) function, derived from fitting a multivariate autoregressive model to the observed signals. A sparse optimization method is proposed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO). In simulations aLASSO was found superior to the commonly used least-squares (LS) estimation with respect to estimation performance. The normalized error between the true and estimated model parameters dropped from 0.200.04 for LS estimatio…