765 research outputs found
Instantaneous nonlinear assessment of complex cardiovascular dynamics by laguerre-volterra point process models
No full textWe report an exemplary study of instantaneous assessment of cardiovascular dynamics performed using point-process nonlinear models based on Laguerre expansion of the linear and nonlinear Wiener-Volterra kernels. As quantifiers, instantaneous measures such as high order spectral features and Lyapunov exponents can be estimated from a quadratic and cubic autoregressive formulation of the model first order moment, respectively. Here, these measures are evaluated on heartbeat series coming from 16 healthy subjects and 14 patients with Congestive Hearth Failure (CHF). Data were gathered from the on-line repository PhysioBank, which has been taken as landmark for testing nonlinear indices. Results show that the proposed nonlinear Laguerre-Volterra point-process methods are able to track the nonlinear and complex cardiovascular dynamics, distinguishing significantly between CHF and healthy heartbeat series. © 2013 IEEE
Assessment of gait nonlinear dynamics by inhomogeneous point-process models
No full textPoint-process linear models of stride intervals have been recently proven to provide a unique characterization of human gait dynamics through instantaneous time domain features. In this study we propose novel instantaneous measures characterizing nonlinear gait dynamics using a quadratic autoregressive inhomogeneous point-process model recently devised for the instantaneous assessment of physiological, natural, and physical discrete dynamical systems. Our mathematical framework accounts for long-term information given by the past events of non-stationary non-Gaussian time series, expressed by a Laguerre expansion of the Wiener-Volterra terms. Here, we present a study of gait variability from data gathered from physionet.org, including 15 recordings from young and elderly healthy volunteers, and patients with Parkinson's disease. Results show that our instantaneous polyspectral characterization provides an informative tracking of the inherent nonlinear dynamics of human gait, which is significantly affected by aging and locomotor disabilities
Disentanglement of sympathetic and parasympathetic activity by instantaneous analysis of human heartbeat dynamics
No full textSpectral analysis of heart rate variability (HRV) is one of the most effective techniques for the assessment of the influence of the autonomic nervous system (ANS) on the heartbeat. Despite its widespread use, it has been demonstrated that HRV subdivision in the low frequency (LF) and high frequency (HF) bands does not accurately reflect separate sympathetic and parasympathetic influences, respectively, mainly due to overlap of the two branches in the low frequencies. Here we propose two novel indices, namely the instantaneous sympathetic autonomic index (SAI) and parasympathetic autonomic index (PAI), that are able to separately assess the time-varying ANS synergic functions. The application of the paradigm is presented here by associating proper combinations of orthonormal Laguerre functions defined within the heartbeat point-process continuous model. Preliminary results from ten subjects recorded during a tilt-table protocol show that the proposed methodology, differently than the traditional spectral parameters, is able to separately track the independent changes associated with the two ANS branches
Instantaneous transfer entropy for the study of cardio-respiratory dynamics
No full textMeasures of transfer entropy have been proposed to quantify the directional coupling and strength between two complex physiological variables. Particular attention has been given to nonlinear interactions within cardiovascular and respiratory dynamics as influenced by the autonomic nervous system. However, standard transfer entropy estimates have shown major limitations in dealing with issues concerning stochastic system modeling, limited observations in time, and the assumption of stationarity of the considered physiological variables. Moreover, standard estimates are unable to track time-varying changes in nonlinear coupling with high resolution in time. Here, we propose a novel definition of transfer entropy linked to inhomogeneous point-process theory. Heartbeat and respiratory dynamics are characterized through discrete time series, and modeled through probability density functions (PDFs) which characterize and predict the time until the occurrence of the next physiological event as a function of the past history. As the derived measures of entropy are instantaneously defined through continuos PDFs, a novel index (the Instantaneous point-process Transfer Entropy, ipT ransfEn) is able to provide instantaneous tracking of the information transfer. The new measure is tested on experimental data gathered from 16 healthy subjects undergoing postural changes, showing fast tracking of the tilting events and low variability during the standing phase
A LightWAVE client for semi-automated annotation of Heart Beats from ECG Time Series
No full textLightWAVE is an open-source web-based software for viewing ECGs and other physiologic waveforms and associated annotations (such as heart-beat markers). At present, most users run the raw ECG through an automated QRS detector and later use LightWAVE to review and correct the detected heart beats. Although this 2-stage procedure may work well with clean signals, it is inefficient and time consuming when the recordings are contaminated by noise, artefacts or recurring ectopic events. To overcome this limitation, we customized the LightWAVE client to allow automated and semi-automated annotation of heart beats from ECG time series. In semi-automatic mode, the algorithm automatically identifies most QRS complexes and stops - asking for manual intervention - whenever the confidence of a detection falls below a given threshold. Additionally, the software now shows the series of inter-beat intervals, which is an invaluable tool to easily spot R-wave misdetections and genuine arrhythmias. The new client introduces further additional features compared to the standard version, for example the possibility of importing raw signals from local CSV files and of exporting the current plot in SVG format. Overall, our customized client extends the functionality of LightWAVE and brings it closer to one of its design goals, i.e. to provide a comfortable and efficient method of annotating physiologic data
Rank-based Multi-Scale Entropy Analysis of Heart Rate Variability
No full textThe method of MultiScale Entropy (MSE) is an invaluable tool to quantify and compare the complexity of physiological time series at different time scales. Although MSE traditionally employs sample entropy to measure the unpredictability of each coarse-grained series, the same framework can be applied to other metrics. Here we investigate the use of a rank-based entropy measure within the MSE framework. Like in the traditional method, the series are studied in an embedding space of dimension m. The novel entropy assesses the unpredictability of the series quantifying the "amount of shuffling" that the ranks of the mutual distances between pairs of m-long vectors undergo when considering the next observation. The algorithm was tested on recordings from the Fantasia database in a time-varying fashion using non-overlapping 300-samples windows. The method was able to find statistically significant differences between young and healthy elderly subjects at 7 scales/time-windows after accounting for multiple comparisons using the Holm-Bonferroni correction. These promising results suggest the possibility of using this measure to perform a time-varying assessment of complexity with increased accuracy and temporal resolution
Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardio-Respiratory Nonstationary Dynamics
No full textObjective: Measures of Transfer Entropy (TE) quantify the direction and strength of coupling between two complex systems. Standard approaches assume stationarity of the observations, and therefore are unable to track time-varying changes in nonlinear information transfer with high temporal resolution. In this study, we aim to define and validate novel instantaneous measures of transfer entropy to provide an im- proved assessment of complex non-stationary cardio-respiratory interactions
Tetravariate point-process model for the continuous characterization of cardiovascular-respiratory dynamics during passive postural changes
No full textIn this study, we present a new methodology for time-varying characterization of cardiovascular (CV) control, which includes RR interval (RRI), systolic arterial pressure (SAP), respiration (RSP) and pulse transit time (PTT). Within a multivariate model, CV dynamics are represented as stochastic point processes whose means has a tetravariate autoregressive structure. Such framework provides the simultaneous time-frequency assessment of: (i) both arms of the SAP-RRI loop, along baroreflex and mechanical feedforward pathways; (ii) Respiratory sinus arrhythmia (RSA), through the direct evaluation of the interactions between RSP and the RRI; (iii) the coupling between cardio-respiratory activity and vascular tone through quantification of the interactions between PTT and the other CV variables. We validated the model by characterizing CV control in 16 healthy subjects during a tilt table test, and we were able to confirm a satisfactory model's goodness-of-ft. We further estimated transfer function gains, instantaneous powers and directed coherences, and observed that RSP strongly drove respiratory-related oscillations in all the other CV variables, and that PTT depended on RRI dynamics rather than blood pressure variations. During head-up tilt, baroreflex sensitivity and RSA decreased, while the gain from RRI to SAP increased, thus confirming previous physiological characterizations. © 2012 CCAL
ECG-Derived Sympathetic and Parasympathetic Activity in the Healthy: An Early Lower-Body Negative Pressure Study Using Adaptive Kalman Prediction
No full textRecent investigations have challenged the reliability of estimating sympathetic autonomic outflow from heart rate variability (HRV) analysis. Towards overcoming this long-lasting challenge, in this study we propose a new formulation for the assessment of autonomic nervous system activity on the heart based on two separate indices: the Sympathetic Activity Index (SAI) and the Parasympathetic Activity Index (PAI). Specifically, considering the RR interval series as an input, we properly combine the output of orthonormal Laguerre filters to disentangle the overlapping contribution of sympathetic and parasympathetic activities on HRV spectra. Adaptive Kalman predictions account for a time-varying SAI and PAI estimation from exemplary data gathered from 35 healthy subjects under-going a lower-body negative pressure (LBNP) protocol. Results show a defined characteristic increase (reduction) of the SAI (PAI) dynamics during LBNP with respect to the resting state condition, demonstrating the reliability of the proposed measures for a non-invasive autonomic assessment in the healthy without the need of individual model calibration. Comparison with standard HRV metrics defined in the frequency domain, as well as prospective endeavours for cardiovascular assessments in pathological states, are also discussed
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