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Giandomenico Nollo - One of the best experts on this subject based on the ideXlab platform.
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Assessing causality in normal and impaired short-term cardiovascular regulation via Nonlinear Prediction methods.
Philosophical Transactions of the Royal Society A: Mathematical Physical and Engineering Sciences, 2009Co-Authors: Giandomenico Nollo, Luca Faes, Renzo Antolini, Alberto PortaAbstract:We investigated the ability of mutual Nonlinear Prediction methods to assess causal interactions in short-term cardiovascular variability during normal and impaired conditions. Directional interactions between heart period (RR interval of the ECG) and systolic arterial pressure (SAP) short-term variability series were quantified as the cross-predictability (CP) of one series given the other, and as the predictability improvement (PI) yielded by the inclusion of samples of one series into the Prediction of the other series. Nonlinear Prediction was performed through global approximation (GA), approximation with locally constant models (LA0) and approximation with locally linear models (LA1) of the Nonlinear function linking the samples of the two series, on patients with neurally mediated syncope and control subjects. Causality measures were evaluated in the two directions (from SAP to RR and from RR to SAP) in the supine (SU) position, in the upright position after head-up tilt (early tilt, ET) and after prolonged upright posture (late tilt, LT). While the trends for the GA, LA0 and LA1 methods were substantially superimposable, PI elicited better than CP the prevalence of causal coupling from RR to SAP during SU. Both CP and PI noted a marked decrease in coupling in both causal directions in syncope subjects during LT, documenting the impairment of cardiovascular regulation in the minutes just preceding syncope.
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A Method for the Time-Varying Nonlinear Prediction of Complex Nonstationary Biomedical Signals
IEEE Transactions on Biomedical Engineering, 2009Co-Authors: Luca Faes $^{ast}$, Ki H Chon, Giandomenico NolloAbstract:A method to perform time-varying (TV) Nonlinear Prediction of biomedical signals in the presence of nonstationarity is presented in this paper. The method is based on identification of TV autoregressive models through expansion of the TV coefficients onto a set of basis functions and on k -nearest neighbor local linear approximation to perform Nonlinear Prediction. The approach provides reasonable Nonlinear Prediction even for TV deterministic chaotic signals, which has been a daunting task to date. Moreover, the method is used in conjunction with a TV surrogate method to provide statistical validation that the presence of Nonlinearity is not due to nonstationarity itself. The approach is tested on simulated linear and Nonlinear signals reproducing both time-invariant (TIV) and TV dynamics to assess its ability to quantify TIV and TV degrees of predictability and detect Nonlinearity. Applicative examples relevant to heart rate variability and EEG analyses are then illustrated.
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Mutual Nonlinear Prediction as a tool to evaluate coupling strength and directionality in bivariate time series: comparison among different strategies based on k nearest neighbors.
Physical Review E, 2008Co-Authors: Luca Faes, Alberto Porta, Giandomenico NolloAbstract:We compare the different existing strategies of mutual Nonlinear Prediction regarding their ability to assess the coupling strength and directionality of the interactions in bivariate time series. Under the common framework of k -nearest neighbor local linear Prediction, we test three approaches based on cross Prediction, mixed Prediction, and predictability improvement. The measures of interdependence provided by these approaches are first evaluated on short realizations of bivariate time series generated by coupled Henon models, investigating also the effects of noise. The usefulness of the three mutual Nonlinear Prediction schemes is then assessed in a common physiological application during known conditions of interaction-i.e., the analysis of the interdependence between heart rate and arterial pressure variability in healthy humans during supine resting and passive head-up tilting. Based on both simulation results and physiological interpretability of cardiovascular results, we conclude that cross Prediction is valuable to quantify the coupling strength and predictability improvement to elicit directionality of the interactions in short and noisy bivariate time series.
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Mutual Nonlinear Prediction of Cardiovascular Variability Series: Comparison between Exogenous and Autoregressive Exogenous Models
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007Co-Authors: Luca Faes, Alberto Porta, Giandomenico NolloAbstract:A model-based approach to perform mutual Nonlinear Prediction of short cardiovascular variability series is presented. The approach is based on identifying exogenous (X) and autoregressive exogenous (ARX) models by K-nearest neighbors local linear approximation, and estimates the predictability of a series given the other as the squared correlation between original and predicted values of the series. The method was first tested on simulations reproducing different types of interaction between non-identical Henon maps, and then applied to heart rate (HR) and blood pressure (BP) variability series measured in healthy subjects at rest and after head-up tilt. Simulations showed that different coupling conditions were always detected by the X model but not by the ARX model. The comparison between X and ARX models suggested the presence of oscillatory sources determining the regularity of HR and BP dynamics independently of their closed-loop mutual regulation. The transition from supine to upright position was associated with an enhancement of the HR and BP mutual regulation, compatible with the activation of the sympathetic nervous system induced by tilt.
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Bivariate Nonlinear Prediction to quantify the strength of complex dynamical interactions in short-term cardiovascular variability
Medical & Biological Engineering & Computing, 2006Co-Authors: Luca Faes, Giandomenico NolloAbstract:A Nonlinear Prediction method for investigating the dynamic interdependence between short length time series is presented. The method is a generalization to bivariate Prediction of the univariate approach based on nearest neighbor local linear approximation. Given the input and output series x and y, the relationship between a pattern of samples of x and a synchronous sample of y was approximated with a linear polynomial whose coefficients were estimated from an equation system including the nearest neighbor patterns in x and the corresponding samples in y. To avoid overfitting and waste of data, the training and testing stages of the Prediction were designed through a specific out-of-sample cross validation. The robustness of the method was assessed on short realizations of simulated processes interacting either linearly or Nonlinearly. The predictor was then used to characterize the dynamical interaction between the short-term spontaneous fluctuations of heart period (RR interval) and systolic arterial pressure (SAP) in healthy young subjects. In the supine position, the predictability of RR given SAP was low and influenced by Nonlinear dynamics. After head-up tilt the predictability increased significantly and was mostly due to linear dynamics. These findings were related to the larger involvement of the baroreflex regulation from SAP to RR in upright than in supine humans, and to the simplification of the RR–SAP coupling occurring with the tilt-induced alteration of the neural regulation of the cardiovascular rhythms.
Alberto Porta - One of the best experts on this subject based on the ideXlab platform.
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Assessing causality in normal and impaired short-term cardiovascular regulation via Nonlinear Prediction methods.
Philosophical Transactions of the Royal Society A: Mathematical Physical and Engineering Sciences, 2009Co-Authors: Giandomenico Nollo, Luca Faes, Renzo Antolini, Alberto PortaAbstract:We investigated the ability of mutual Nonlinear Prediction methods to assess causal interactions in short-term cardiovascular variability during normal and impaired conditions. Directional interactions between heart period (RR interval of the ECG) and systolic arterial pressure (SAP) short-term variability series were quantified as the cross-predictability (CP) of one series given the other, and as the predictability improvement (PI) yielded by the inclusion of samples of one series into the Prediction of the other series. Nonlinear Prediction was performed through global approximation (GA), approximation with locally constant models (LA0) and approximation with locally linear models (LA1) of the Nonlinear function linking the samples of the two series, on patients with neurally mediated syncope and control subjects. Causality measures were evaluated in the two directions (from SAP to RR and from RR to SAP) in the supine (SU) position, in the upright position after head-up tilt (early tilt, ET) and after prolonged upright posture (late tilt, LT). While the trends for the GA, LA0 and LA1 methods were substantially superimposable, PI elicited better than CP the prevalence of causal coupling from RR to SAP during SU. Both CP and PI noted a marked decrease in coupling in both causal directions in syncope subjects during LT, documenting the impairment of cardiovascular regulation in the minutes just preceding syncope.
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Mutual Nonlinear Prediction as a tool to evaluate coupling strength and directionality in bivariate time series: comparison among different strategies based on k nearest neighbors.
Physical Review E, 2008Co-Authors: Luca Faes, Alberto Porta, Giandomenico NolloAbstract:We compare the different existing strategies of mutual Nonlinear Prediction regarding their ability to assess the coupling strength and directionality of the interactions in bivariate time series. Under the common framework of k -nearest neighbor local linear Prediction, we test three approaches based on cross Prediction, mixed Prediction, and predictability improvement. The measures of interdependence provided by these approaches are first evaluated on short realizations of bivariate time series generated by coupled Henon models, investigating also the effects of noise. The usefulness of the three mutual Nonlinear Prediction schemes is then assessed in a common physiological application during known conditions of interaction-i.e., the analysis of the interdependence between heart rate and arterial pressure variability in healthy humans during supine resting and passive head-up tilting. Based on both simulation results and physiological interpretability of cardiovascular results, we conclude that cross Prediction is valuable to quantify the coupling strength and predictability improvement to elicit directionality of the interactions in short and noisy bivariate time series.
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Complexity and Nonlinearity in Short-Term Heart Period Variability: Comparison of Methods Based on Local Nonlinear Prediction
IEEE Transactions on Biomedical Engineering, 2007Co-Authors: Alberto Porta, Stefano Guzzetti, Raffaello Furlan, Tomaso Gnecchi-ruscone, Nicola Montano, Alberto MallianiAbstract:This paper evaluates the paradigm that proposes to quantify short-term complexity and detect Nonlinear dynamics by exploiting local Nonlinear Prediction. Local Nonlinear Prediction methods are classified according to how they judge similarity among patterns of L samples (i.e., according to different definitions of the cells utilized to discretize the phase space) and examined in connection with different types of surrogate data: 1) phase-randomized or Fourier transform based, FT; 2) amplitude-adjusted FT, AAFT; 3) iteratively-refined AAFT, IAAFT, preserving distribution IAAFT-1; 4) IAAFT preserving power spectrum, IAAFT-2. The methods were applied on ad-hoc simulations and on a large database of short heart period variability series (~300 cardiac beats) recorded in healthy young subjects during experimental conditions inducing a sympathetic activation (head-up tilt, infusion of nitroprusside, or handgrip), a parasympathetic activation (low dose administration of atropine or infusion of phenylephrine), a complete parasympathetic blockade (high dose administration of atropine), or during controlled respiration at different breathing rates. As to complexity analysis we found that: 1) although complexity indexes derived from different methods were different in terms of absolute values, changes due to experimental conditions were consistently detected; 2) complexity was significantly decreased by all the experimental conditions provoking a sympathetic activation and by controlled respiration at slow breathing rates. As to detection of Nonlinearities we found that: 1) IAAFT-1 and IAAFT-2 surrogates performed similarly in all protocols; 2) FT and IAAFT surrogates detected about the same percentage of Nonlinear dynamics in all protocols; 3) AAFT surrogates were inappropriate with all the methods and should be dismissed in future applications; 4) methods based on overlapping cells with variable size were characterized by a larger rate of false detections of Nonlinear dynamics; 5) short-term heart period variability at rest was mostly linear; 6) controlled respiration at slow breathing rates increased Nonlinear components, while the separate activation of the two branches of the autonomic nervous system (i.e., sympathetic or parasympathetic) was ineffective at this regard
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Mutual Nonlinear Prediction of Cardiovascular Variability Series: Comparison between Exogenous and Autoregressive Exogenous Models
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007Co-Authors: Luca Faes, Alberto Porta, Giandomenico NolloAbstract:A model-based approach to perform mutual Nonlinear Prediction of short cardiovascular variability series is presented. The approach is based on identifying exogenous (X) and autoregressive exogenous (ARX) models by K-nearest neighbors local linear approximation, and estimates the predictability of a series given the other as the squared correlation between original and predicted values of the series. The method was first tested on simulations reproducing different types of interaction between non-identical Henon maps, and then applied to heart rate (HR) and blood pressure (BP) variability series measured in healthy subjects at rest and after head-up tilt. Simulations showed that different coupling conditions were always detected by the X model but not by the ARX model. The comparison between X and ARX models suggested the presence of oscillatory sources determining the regularity of HR and BP dynamics independently of their closed-loop mutual regulation. The transition from supine to upright position was associated with an enhancement of the HR and BP mutual regulation, compatible with the activation of the sympathetic nervous system induced by tilt.
Luca Faes $^{ast}$ - One of the best experts on this subject based on the ideXlab platform.
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A Method for the Time-Varying Nonlinear Prediction of Complex Nonstationary Biomedical Signals
IEEE Transactions on Biomedical Engineering, 2009Co-Authors: Luca Faes $^{ast}$, Ki H Chon, Giandomenico NolloAbstract:A method to perform time-varying (TV) Nonlinear Prediction of biomedical signals in the presence of nonstationarity is presented in this paper. The method is based on identification of TV autoregressive models through expansion of the TV coefficients onto a set of basis functions and on k -nearest neighbor local linear approximation to perform Nonlinear Prediction. The approach provides reasonable Nonlinear Prediction even for TV deterministic chaotic signals, which has been a daunting task to date. Moreover, the method is used in conjunction with a TV surrogate method to provide statistical validation that the presence of Nonlinearity is not due to nonstationarity itself. The approach is tested on simulated linear and Nonlinear signals reproducing both time-invariant (TIV) and TV dynamics to assess its ability to quantify TIV and TV degrees of predictability and detect Nonlinearity. Applicative examples relevant to heart rate variability and EEG analyses are then illustrated.
Jin Zhou - One of the best experts on this subject based on the ideXlab platform.
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Feature analysis of epileptic EEG using Nonlinear Prediction method
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010Co-Authors: Qingfang Meng, Weidong Zhou, Yuehui Chen, Jin ZhouAbstract:We propose a feature extraction method based on the Volterra autoregressive model's Prediction power and the data's predictability for the EEG signals to automatically detect the epileptic EEG signals from the EEG recordings. The method of determining the embedding dimension based on Nonlinear Prediction is applied to choose the embedding dimension of the EEG data. The proposed feature extraction method is used to extract the feature for three groups of EEG time series composing epileptic seizure. We divide the EEG data into segments, and respectively compute the feature values of each segment, where the length of data segment respectively takes the value of 250, 500, 1000 points. To investigate the robustness of our method under noises, we also analyze the three EEG time series with additive white Gaussian noise. The experiment results show that the feature values extracted with the proposed method could obviously distinguish the epileptic EEG signals from the normal EEG signals. The proposed method is effective for short time series, insensitive to the length of data segment, and robust to the additive white noise, and it could differentiate the epileptic EEG from the normal EEG when the signal-to-noise ratio is low.
Ki H Chon - One of the best experts on this subject based on the ideXlab platform.
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A Method for the Time-Varying Nonlinear Prediction of Complex Nonstationary Biomedical Signals
IEEE Transactions on Biomedical Engineering, 2009Co-Authors: Luca Faes $^{ast}$, Ki H Chon, Giandomenico NolloAbstract:A method to perform time-varying (TV) Nonlinear Prediction of biomedical signals in the presence of nonstationarity is presented in this paper. The method is based on identification of TV autoregressive models through expansion of the TV coefficients onto a set of basis functions and on k -nearest neighbor local linear approximation to perform Nonlinear Prediction. The approach provides reasonable Nonlinear Prediction even for TV deterministic chaotic signals, which has been a daunting task to date. Moreover, the method is used in conjunction with a TV surrogate method to provide statistical validation that the presence of Nonlinearity is not due to nonstationarity itself. The approach is tested on simulated linear and Nonlinear signals reproducing both time-invariant (TIV) and TV dynamics to assess its ability to quantify TIV and TV degrees of predictability and detect Nonlinearity. Applicative examples relevant to heart rate variability and EEG analyses are then illustrated.
