Publications by authors named "Oscar De Feo"

Background: We study here the influence of different patients and the influence of different devices with the same patients on the signals and modeling of data from measurements from a noninvasive Multisensor glucose monitoring system in patients with type 1 diabetes. The Multisensor includes several sensors for biophysical monitoring of skin and underlying tissue integrated on a single substrate.

Method: Two Multisensors were worn simultaneously, 1 on the upper left and 1 on the upper right arm by 4 patients during 16 study visits.

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Background: Impedance spectroscopy has been shown to be a candidate for noninvasive continuous glucose monitoring in humans. However, in addition to glucose, other factors also have effects on impedance characteristics of the skin and underlying tissue.

Method: Impedance spectra were summarized through a principal component analysis and relevant variables were identified with Akaike's information criterion.

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The extraction of information from measured data about the interactions taking place in a network of systems is a key topic in modern applied sciences. This topic has been traditionally addressed by considering bivariate time series, providing methods which are sometimes difficult to extend to multivariate data, the limiting factor being the computational complexity. Here, we present a computationally viable method based on black-box modeling which, while theoretically applicable only when a deterministic hypothesis about the processes behind the recordings is plausible, proves to work also when this assumption is severely affected.

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We investigate the families of periodic and nonperiodic behaviors admitted by a hysteresis-based circuit oscillator. The analysis is carried out by combining brute-force simulations with continuation methods. As a result of the analysis, it is shown that the existence of many different periodic solutions and of the chaotic behaviors associated with them is organized by few codimension-2 bifurcation points.

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Background: The cortical representation of the visual field is split along the vertical midline, with the left and the right hemi-fields projecting to separate hemispheres. Connections between the visual areas of the two hemispheres are abundant near the representation of the visual midline. It was suggested that they re-establish the functional continuity of the visual field by controlling the dynamics of the responses in the two hemispheres.

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Background: The dysconnection hypothesis has been proposed to account for pathophysiological mechanisms underlying schizophrenia. Widespread structural changes suggesting abnormal connectivity in schizophrenia have been imaged. A functional counterpart of the structural maps would be the EEG synchronization maps.

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In the present paper we propose a novel method for the identification and modeling of neural networks using extracellular spike recordings. We create a deterministic model of the effective network, whose dynamic behavior fits experimental data. The network obtained by our method includes explicit mathematical models of each of the spiking neurons and a description of the effective connectivity between them.

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Cortical computation involves the formation of cooperative neuronal assemblies characterized by synchronous oscillatory activity. A traditional method for the identification of synchronous neuronal assemblies has been the coherence analysis of EEG signals. Here, we suggest a new method called S estimator, whereby cortical synchrony is defined from the embedding dimension in a state-space.

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Various forms of chaotic synchronization have been proposed as ways of realizing associative memories and/or pattern recognizers. To exploit this kind of synchronization phenomena in temporal pattern recognition, a chaotic dynamical system representing the class of signals that are to be recognized must be established. This system can be determined by means of identification techniques.

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