1,721,074 research outputs found
Parameter Estimation
No full textIn general, a model describes-through suitable equations-the relationship between some inputs and some (measurable) outputs. This relationship is constituted from two parts: the model structure, that is, the mathematical law that describes a family of possible behaviors and the model parameters, that is, those quantities that can vary from one situation to another and that modulate and completely define the relationship. Assuming that model structure is known, the present chapter presents the basic concepts and techniques for parameter estimation (also called model identification), that is, the capability of deriving numerical values for model parameters from a set of noisy measurements. In particular, by using suitable case studies taken from the literature, Fisherian (e.g., least squares, maximum likelihood) and Bayesian estimators (e.g., maximum a posteriori) are presented, the latter also with probability distributions handled through Markov chain Monte Carlo simulation techniques. Analysis of the residuals for model checking and computation of the parameter estimate precisions are also discussed. © 2014 Elsevier Inc. All rights reserved
Erratum: Enhanced accuracy of continuous glucose monitoring by online extended kalman filtering (Diabetes Technology & Therapeutics (2010) 12:5 (353-363))
No full textA neural network predicting the amplitude of the N2pc in individual EEG datasets
Get PDFObjective. The N2pc is a small amplitude transient interhemispheric voltage asymmetry used in cognitive neuroscience to investigate subject's allocation of selective visuo-spatial attention. N2pc is typically estimated by averaging the sweeps of the electroencephalographic (EEG) signal but, in absence of explicit normative indications, the number of sweeps is often based on arbitrariness or personal experience. With the final aim of reducing duration and cost of experimental protocols, here we developed a new approach to reliably predict N2pc amplitude from a minimal EEG dataset. Approach. First, features predictive of N2pc amplitude were identified in the time-frequency domain. Then, an artificial neural network (NN) was trained to predict N2pc mean amplitude at the individual level. By resorting to simulated data, accuracy of the NN was assessed by computing the mean squared error (MSE) and the amplitude discretization error (ADE) and compared to the standard time averaging (TA) technique. The NN was then tested against two real datasets consisting of 14 and 12 subjects, respectively. Main result. In simulated scenarios entailing different number of sweeps (between 10 and 100), the MSE obtained with the proposed method resulted, on average, 1/5 of that obtained with the TA technique. Implementation on real EEG datasets showed that N2pc amplitude could be reliably predicted with as few as 40 EEG sweeps per cell of the experimental design. Significance. The developed approach allows to reduce duration and cost of experiments involving the N2pc, for instance in studies investigating attention deficits in pathological subjects
Accurate hemodynamic response estimation by removal of stimulus-evoked superficial response in fNIRS signals
Get PDFObjective. We address the problem of hemodynamic response (HR) estimation when task-evoked extra-cerebral components are present in functional near-infrared spectroscopy (fNIRS) signals. These components might bias the HR estimation; therefore, careful and accurate denoising of data is needed. Approach. We propose a dictionary-based algorithm to process each single event-related segment of the acquired signal for both long separation (LS) and short separation (SS) channels. Stimulus-evoked components and physiological noise are modeled by means of two distinct waveform dictionaries. For each segment, after removal of the physiological noise component in each channel, a template is employed to estimate stimulus-evoked responses in both channels. Then, the estimate from the SS channel is employed to correct the evoked superficial response and refine the HR estimate from the LS channel. Main results. Analysis of simulated, semi-simulated and real data shows that, by averaging single-segment estimates over multiple trials in an experiment, reliable results and improved accuracy compared to other methods can be obtained. The average estimation error of the proposed method for the semi-simulated data set is 34% for oxy-hemoglobin (HbO) and 78% for deoxy-hemoglobin (HbR), considering 40 trials. The proposed method outperforms the results of the methods proposed in the literature. While still far from the possibility of single-trial HR estimation, a significant reduction in the number of averaged trials can also be obtained. Significance. This work proves that dedicated dictionaries can be successfully employed to model all different components of fNIRS signals. We demonstrate the effectiveness of a specifically designed algorithm structure in dealing with a complex denoising problem, enhancing the possibilities of fNIRS-based HR analysis
Development of an error model for a factory-calibrated continuous glucose monitoring sensor with 10-day lifetime
Get PDFFactory-calibrated continuous glucose monitoring (FC-CGM) sensors are new devices used in type 1 diabetes (T1D) therapy to measure the glucose concentration almost continuously for 10–14 days without requiring any in vivo calibration. Understanding and modelling CGM errors is important when designing new tools for T1D therapy. Available literature CGM error models are not suitable to describe the FC-CGM sensor error, since their domain of validity is limited to 12-h time windows, i.e., the time between two consecutive in vivo calibrations. The aim of this paper is to develop a model of the error of FC-CGM sensors. The dataset used contains 79 FC-CGM traces collected by the Dexcom G6 sensor. The model is designed to dissect the error into its three main components: effect of plasma-interstitium kinetics, calibration error, and random measurement noise. The main novelties are the model extension to cover the entire sensor lifetime and the use of a new single-step identification procedure. The final error model, which combines a first-order linear dynamic model to describe plasma-interstitium kinetics, a second-order polynomial model to describe calibration error, and an autoregressive model to describe measurement noise, proved to be suitable to describe FC-CGM sensor errors, in particular improving the estimation of the physiological time-delay
Algorithm based on the "dynamic risk" concept allows ahead-of-time suggestion of corrective insulin boluses in type 1 diabetes therapies employing continuous glucose monitors
No full textDetection of the N2/N2pc event-related potential (ERP) components buried in the EEG using phase angle distribution across sweeps
No full textThe N2pc event-related potential component measures direction and time course of selective visual attention and represents an important biomarker in cognitive neuroscience. While its subtractive origin strongly influences the amplitude, thus hindering its detection, other external factors, such as subject’s inefficiency to allocate attention to the cued target, or the heterogeneity of the visual context, may strongly affect the elicitation of the component itself. It would therefore be extremely important to create a tool that, using as few sweeps as possible, could reliably establish whether an N2pc is present in an individual subject. In the present work, we propose an approach by resorting to a time-frequency analysis of N2pc individual signals; in particular, power at each frequency band (α/β/δ/θ) was computed in the N2 time range and correlated to the estimated amplitude of the N2pc. Preliminary results on fourteen human volunteers of a visual search design showed a very high correlation coefficient (over 0.9) between the low frequency bands power and the mean absolute amplitude of the component, using only 40 sweeps. Results also seemed to suggest that N2pc amplitude values higher than 0.5 μV could be accurately classified according to time-frequency indices. Clinical Relevance — The online detection of the N2pc presence in individual EEG datasets would allow not only to study the factors responsible of N2pc variability across subjects and conditions, but also to investigate novel search variants on participants with a predisposition to show an N2pc, reducing time and costs and the possibility to obtain biased results
Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices
No full textAn online failure detection method of the glucose-insulin pump system: Improved overnight safety of type 1 diabetes subjects
No full textAutomated analysis of local field potentials evoked by mechanical whisker stimulation in rat barrel cortex
No full textLocal field potentials (LFPs) recorded in the barrel cortex in rats and mice are important to investigate somatosensory systems, the final aim being to start to understand mechanisms of brain representation of sensory stimuli in humans. Parameters extracted from LFP of particular interest include spike timing and transmembrane current flow. Recent improvements in microelectrodes technology have enabled neuroscientists to acquire a great amount of LFP signals during the same experimental session, calling for the development of algorithms for their quantitative automatic analysis. In the present work, an algorithm based on Phillips-Tikhonov regularization is presented to automatically detect the main features (in terms of amplitude and latency) of LFP waveforms recorded after whisker stimulation in rat. The accuracy of the algorithm is first assessed in a Monte Carlo simulation mimicking the acquisition of LFP in three different conditions of SNR. Then, the algorithm is tested by analyzing a set of 100 LFP recorded in the primary somatosensory (S1) cortex, i.e., the region involved in the cortical representation of touch in mammals
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