Abstract
Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation. (C) 2013 AIP Publishing LLC

Abstract
During surgical interventions, a muscle relaxant drug is frequently administered with the objective of inducing muscle paralysis. Clinical environment and patient safety issues lead to a huge variety of situations that must be taken into account requiring intensive simulation studies. Hence, population models are crucial for research and development in this field.This work develops a stochastic population model for the neuromuscular blockade (NMB) (muscle paralysis) level induced by atracurium based on a deterministic individual model already proposed in the literature. To achieve this goal, a joint Lognormal distribution is considered for the patient-dependent parameters. This study is based on clinical data collected during general anaesthesia. The procedure developed enables to construct a reliable reference bank of parametrized models that not only reproduces the overall features of the NMB, but also the inter-individual variability characteristic of physiological signals. It turns out that this bank constitutes a fundamental tool to support research on identification and control algorithms and is suitable to be integrated in clinical decision support systems.

Abstract
In the last decades propofol became established as an intravenous agent for the induction and maintenance of both sedation and general anesthesia procedures. In order to achieve the desired clinical effects appropriate infusion rate strategies must be designed. Moreover, it is important to avoid or minimize associated side effects namely adverse cardiorespiratory effects and delayed recovery. Nowadays, to attain these purposes the continuous propofol delivery is usually performed through target-controlled infusion (TCI) systems whose algorithms rely on pharmacokinetic and pharmacodynamic models. This work presents statistical models to estimate both the infusion rate and the bolus administration. The modeling strategy relies on multivariate linear models, based on patient characteristics such as age, height, weight and gender along with the desired target concentration. A clinical database collected with a RugLoopII device on 84 patients undergoing ultrasonographic endoscopy under sedation-analgesia with propofol and remifentanil is used to estimate the models (training set with 74 cases) and assess their performance (test set with 10 cases). The results obtained in the test set comprising a broad range of characteristics are satisfactory since the models are able to predict bolus, infusion rates and the effect-site concentrations comparable to those of TCI. Furthermore, comparisons of the effect-site concentrations for dosages predicted by the proposed Linear model and the Marsh model for the same target concentration is achieved using Schnider model and a factorial design on the factors (patients characteristics). The results indicate that the Linear model predicts a dosage profile that is faster in leading to an effect-site concentration closer to the desired target concentration.

Abstract
Dysfunctions of the autonomic nervous system in critically ill patients with Acute Brain Injury (ABI) lead to changes in Heart Rate Variability (HRV) which appear to be particularly marked in patients subsequently declared in Brain Death (BD). HRV series are non-stationary, exhibit long memory in the mean and time-varying conditional variance (volatility), characteristics that are well modeled by AutoRegressive Fractionally Integrated Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. The long memory is estimated by the parameter d of the ARFIMA-GARCH model, whilst the time-varying conditional variance parameters, u and v characterize, respectively, the short-range and the persistence in the conditional variance. In this work, the ARFIMA-GARCH approach is applied to HRV series of 15 pediatric patients with ABI admitted in a pediatric intensive care unit, 5 of which has BD confirmed and 9 patients survived. The long memory and time-varying conditional variance parameters estimated by ARFIMA-GARCH modeling significantly differ between groups and seem able to contribute to characterize disease severity in children with ABI.

Abstract
Long recordings of heart rate variability (HRV) display non-stationary characteristics and exhibit long- and short-range correlations. The nonparametric methodology detrended fluctuation analysis (DFA) has become a widely used technique for the detection of long-range correlations in non-stationary HRV data. Recently, we have proposed an alternative approach based on fractional integrated autoregressive moving average (ARFIMA) modelling. These models are an extension of the AR models usual in HRV analysis and have special interest for applications because of their ability for modelling both short- and long-term behaviour of a time series. In this work, DFA is used to assess also short-range scales, further characterizing the data. The methods are applied to 24 h HRV recordings from the Noltisalis database, collected from healthy subjects, patients suffering from congestive heart failure and heart transplanted patients. The analysis of short-range scales leads to a better discrimination between the different groups. © 2013, Springer-Verlag Berlin Heidelberg.

Abstract
Modeling Heart Rate Variability (HRV) data has become important for clinical applications and as a research tool. These data exhibit long memory and time-varying conditional variance (volatility). In HRV, volatility is traditionally estimated by recursive least squares combined with short memory AutoRegressive (AR) models. This work considers a parametric approach based on long memory Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with heteroscedastic errors. To model the heteroscedasticity nonlinear Generalized Autoregressive Conditionally Heteroscedastic (GARCH) and Exponential Generalized Autoregressive Conditionally Heteroscedastic (EGARCH) models are considered. The latter are necessary to model empirical characteristics of conditional volatility such as clustering and asymmetry in the response, usually called leverage in time series literature. The ARFIMA-EGARCH models are used to capture and remove long memory and characterize conditional volatility in 24 hour HRV recordings from the Noltisalis database.