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This study aims at characterizing the short-term time-courses of time- and frequency-domain heart rate variability (HRV) parameters during head-up tilt test (HUTT). Data from 44 young patients with a history of syncope and 34 age-matched controls was analysed in two age-groups related to puberty (<13 and >14 years), and separately for gender, by extracting minute-by-minute progression of mean...
A multi-input modeling approach is introduced to quantify hippocampal neural dynamics. It is based on the Volterra modeling approach extended to multiple inputs. The computed Volterra kernels allow quantitative description of hippocampal transformations and define a predictive model that can produce responses to arbitrary input patterns. Electrophysiological data from several CA3 and CA1 cells in...
Susceptibility-weighted magnetic resonance imaging is a powerful tool for high resolution imaging of the vasculature, aiding in the diagnosis of many pathologic conditions. The technique is especially beneficial at higher field strengths where traditional sequences that measure cerebral blood volume suffer from severe distortions, rendering them inapplicable at 7 T. However, conventional susceptibility-weighted...
In this paper we investigated on the possibility of using a wearable kinesthetic system for monitoring flexion-extension of the knee joint during deambulation. The final goal was to provide an unobtrusive technique to identify the clinical stage of the recovery process of patients affected by venous ulcer. These patients have great difficulty in walking depending on the severity level of disease and...
In this paper we consider the sensitivity analysis of a model of the cardiovascular system (CVS) simulating the transition to aerobic exercise and where the control for the system is implemented via an optimal control. Classical and generalized sensitivity analysis are discussed and compared and their application to the CVS model is analyzed
The purpose of this study is to develop and evaluate a probabilistic framework for reliability analysis of information-theoretic computer-assisted detection (IT-CAD) systems in mammography. The study builds upon our previous work on a feature-based reliability analysis technique tailored to traditional CAD systems developed with a supervised learning scheme. The present study proposes a probabilistic...
Rheoencephalography (REG) is impedance plethysmography applied to the head, and provides an indirect measurement of the pulsatility of the cerebral blood volume. To extend REG as a clinical and research tool, it is necessary to evaluate the sensitivity of REG measurement to local brain conductivity changes. By means of the analytical solution of a four-sphere geometrical model of the head, maps of...
The issue of subject-specific parameter selection in an electroencephalogram (EEG)-based brain-computer interface (BCI) is tackled in this paper. Hjorth- and Barlow-based feature extraction procedures (FEPs) are investigated along with linear discriminant analysis (LDA) for classification. These are well-known nonparametric FEPs but their simplicity prevents them from matching the performance of more...
The aim of this study was to carry out a comparison of different linear and non-linear models from different centres on a common dataset in a double-blind manner to eliminate bias. The dataset was shared over the Internet using a secure bespoke environment called geoconda. Models evaluated included: (1) Cox model, (2) Log Normal model, (3) Partial Logistic Spline, (4) Partial Logistic Artificial Neural...
Artificial neural networks can be trained to predict outcomes in a neonatal intensive care unit (NICU). This paper expands on past research and shows that neural networks trained by the maximum likelihood estimation criterion will approximate the `a posteriori probability' of NICU mortality. A gradient ascent method for the weight update of three-layer feed-forward neural networks was derived. The...
In this paper, we present a comprehensive neural network based modeling and validation framework for reverse engineering gene regulatory interactions. We employ two approaches, Gene Set Stochastic Sampling and Sensitivity Analysis, to infer these interactions. We first apply these methods to a simulated artificial dataset to ensure their correctness and accuracy. True biological interactions are then...
Previously, we built a pulmonary and cardiovascular integrated model which is driven by O2 consumption. However this model is adjusted to average person and cannot correspond to individual difference. It needs parameter adjustment to be applied for simulation of a particular person. We used lumped parameter models, which have simple structures and are well suited to macro-model. However they have...
Obstructive sleep apnea syndrome (OSAS) is observed in approximately 2% of children. Heart rate variability (HRV) is a potentially simple, non-invasive diagnostic screening tool for OSAS. In this study, we investigated the diagnostic potential of HRV using power spectral analysis, numerical titration, sample entropy, and detrended fluctuation analysis. Effects of sleep stages (REM and NREM sleep)...
This paper presents an orthopedic stabiliser to be used for the treatment of periarticular fractures of the knee joint. The design of the device is documented by experimental research of the knee joint kinematics. Experimental and simulation research suggest the use of a four-bar linkage mechanism. This research has also made it possible to define the range of adjustment to adapt the stabiliser kinematics...
In this paper we consider the sensitivity analysis of a model of the cardiovascular system (CVS) simulating the transition to aerobic exercise and where the control for the system is implemented via an optimal control. Classical and generalized sensitivity analysis are discussed and compared and their application to the CVS model is analyzed
The issue of subject-specific parameter selection in an electroencephalogram (EEG)-based brain-computer interface (BCI) is tackled in this paper. Hjorth- and Barlow-based feature extraction procedures (FEPs) are investigated along with linear discriminant analysis (LDA) for classification. These are well-known nonparametric FEPs but their simplicity prevents them from matching the performance of more...
Previously, we built a pulmonary and cardiovascular integrated model which is driven by O2 consumption. However this model is adjusted to average person and cannot correspond to individual difference. It needs parameter adjustment to be applied for simulation of a particular person. We used lumped parameter models, which have simple structures and are well suited to macro-model. However they have...
We present a new method for single trial detection of P300 evoked responses. The features used to classify are the coefficients of a least-squares fit of a single EEG epoch to the intrinsical mode functions of an empirical mode decomposition of the averaged event response from a P300 training set. Support vector machines with a linear kernel are used to classify the epochs and receiver operating characteristic...
This study aims at characterizing the short-term time-courses of time- and frequency-domain heart rate variability (HRV) parameters during head-up tilt test (HUTT). Data from 44 young patients with a history of syncope and 34 age-matched controls was analysed in two age-groups related to puberty (<13 and >14 years), and separately for gender, by extracting minute-by-minute progression of mean...
Obstructive sleep apnea syndrome (OSAS) is observed in approximately 2% of children. Heart rate variability (HRV) is a potentially simple, non-invasive diagnostic screening tool for OSAS. In this study, we investigated the diagnostic potential of HRV using power spectral analysis, numerical titration, sample entropy, and detrended fluctuation analysis. Effects of sleep stages (REM and NREM sleep)...
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