Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Introduction
- Inference and learning in latent Markov models
- Part I State space methods for neural data
- Part II State space methods for clinical data
- Bayesian nonparametric learning of switching dynamics in cohort physiological time series: application in critical care patient monitoring
- Identifying outcome-discriminative dynamics in multivariate physiological cohort time series
- A dynamic point process framework for assessing heartbeat dynamics and cardiovascular functions
- Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression
- Signal quality indices for state space electrophysiological signal processing and vice versa
- index
- References
A dynamic point process framework for assessing heartbeat dynamics and cardiovascular functions
from Part II - State space methods for clinical data
Published online by Cambridge University Press: 05 October 2015
Edited by
Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Introduction
- Inference and learning in latent Markov models
- Part I State space methods for neural data
- Part II State space methods for clinical data
- Bayesian nonparametric learning of switching dynamics in cohort physiological time series: application in critical care patient monitoring
- Identifying outcome-discriminative dynamics in multivariate physiological cohort time series
- A dynamic point process framework for assessing heartbeat dynamics and cardiovascular functions
- Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression
- Signal quality indices for state space electrophysiological signal processing and vice versa
- index
- References
Summary
Introduction
Modeling physiological systems by control systems theory, advanced signal processing, and parametric modeling and estimation approaches has been of focal importance in biomedical engineering (Khoo 1999; Marmarelis 2004; Xiao et al. 2005; Porta et al. 2009). In computerized cardiology, various types of data such as the electrocardiogram (ECG), arterial blood pressure (ABP, measured by invasive arterial line catheters or noninvasive finger cuffs), and respiratory effort (RP; e.g., measured by plethysmography or by piezoelectric respiratory belt transducers) are recorded, digitized and saved to a computer to be available for off-line analysis. Modeling autonomic cardiovascular control using mathematical approaches is helpful for understanding and assessing autonomic cardiovascular functions in healthy or pathological subjects (Berntson et al. 1997; Parati et al. 2001; Stauss 2003; Eckberg 2008). Continuous quantification of heartbeat dynamics, as well as their interactions with other cardiovascular measures, have been widely studied in the past decades (Baselli et al. 1988; Saul & Cohen 1994; Chon et al. 1996; Barbieri et al. 2001; Porta et al. 2002).
A central goal in biomedical engineering applied to cardiovascular control is to develop quantitative measures and informative indices that can be extracted from physiological measurements. Assessing and monitoring informative physiological indices is an important task in both clinical practice and laboratory research. Specifically, a major challenge in cardiovascular engineering is to develop statistical models and apply signal processing tools to investigate various cardiovascular-respiratory functions, such as heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and baroreflex sensitivity (BRS). In the literature, numerous methods have been proposed for quantitative HRV analysis (Malpas 2002). Two types of standard methods are widely used, one is time-domain analysis based on heartbeat intervals, the other is frequency-domain analysis (Baselli et al. 1985). In addition, nonlinear system identification methods have also been applied to heartbeat interval analysis (Chon et al. 1996; Christini et al. 1995; Zhang et al. 2004; Xiao et al. 2005). Examples of higher-order characterization for cardiovascular signals include nonlinear autoregressive (AR) models, Volterra–Wiener series expansion, and Volterra–Laguerre models (Korenberg 1991; Marmarelis 1993; Akay 2000).
- Type
- Chapter
- Information
- Advanced State Space Methods for Neural and Clinical Data , pp. 302 - 329Publisher: Cambridge University PressPrint publication year: 2015
References
Akay|M. (2000). Nonlinear Biomedical Signal Processing, Volume II: Dynamic Analysis and Modeling, New York: Wiley-IEEE Press.
& (2006). Analysis of heartbeat dynamics by point process adaptive filtering. IEEE Transactions on Biomedical Engineering 53(1), 4–12.Google Scholar
, , & (2005). A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. American Journal of Physiology – Heart and Circulatory Physiology 288(1), 424–435.Google Scholar
, & (2001). Closed- versus open-loop assessment of heart rate baroreflex. IEEE Magazine on Engineering in Medicine and Biology 20(2), 33–42.Google Scholar
, , & (1985). Autoregressive modeling and power spectral estimate of R-R interval time series in arrhythmic patients. Computers and Biomedical Research 18(6), 510–530.Google Scholar
, , , & (1988). Cardiovascular variability signals: towards the identification of a closed-loop model of the neural control mechanisms. IEEE Transactions on Biomedical Engineering 35, 1033–1046.Google Scholar
, , , , , , , , , & (1997). Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 34, 623–648.Google Scholar
(2005). The theory of point processes for neural systems. In , , , & , eds, Methods and Models in Neurophysics, Amsterdam: Elsevier, pp. 691–726.
, , , & (2001). An analysis of neural receptive field plasticity by point process adaptive filtering. Proceedings of National Academy of Sciences USA 98, 12261–12266.Google Scholar
, & (2008). A study of probabilistic models for characterizing human heart beat dynamics in autonomic blockade control. In Proceedings of ICASSP'08, pp. 481–484.Google Scholar
, & (2009a). Assessment of autonomic control and respiratory sinus arrhythmia using point process models of human heart beat dynamics. IEEE Transactions on Biomedical Engineering 56(7), 1791–1802.Google Scholar
, & (2010b). Characterizing nonlinear heartbeat dynamics within a point process framework. IEEE Transactions on Biomedical Engineering 57(6), 1335–1347.Google Scholar
, , , & (2011b). Instantaneous assessment of autonomic cardiovascular control during general anesthesia. In Proceedings of IEEE Conference on Engineering in Medicine and Biology, pp. 8444–8447.Google Scholar
, , & (2010a). A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis. In Proceedings of IEEE Conference on Engineering in Medicine and Biology, pp. 3567–3570.Google Scholar
, , & (2013). A unified point process probabilistic framework to assess heartbeat dynamics and autonomic cardiovascular control. Frontiers in Computational Physiology and Medicine 3, 1–14.Google Scholar
, , , , , & (2011a). Dynamic assessment of baroreflex control of heart rate during induction of propofol anesthesia using a point process method. Annals of Biomedical Engineering 39(1), 260–279.Google Scholar
, , , , , , , & (2009b). Linear and nonlinear quantification of respiratory sinus arrhythmia during propofol general anesthesia. In Proceedings of IEEE Conference on Engineering in Medicine and Biology, pp. 5336–5339.Google Scholar
, , , , & (1997). Linear and nonlinear system identification of autonomic heart-rate modulation. IEEE Magazine on Engineering in Medicine and Biology 16, 96–105.Google Scholar
, & (1996). A dual-input nonlinear system analysis of autonomic modulation of heart rate. IEEE Transactions on Biomedical Engineering 43, 530–540.Google Scholar
, , , , & (1995). Application of linear and nonlinear time series modeling to heart rate dynamics analysis. IEEE Transactions on Biomedical Engineering 42, 411–415.Google Scholar
, & (2002). Multiscale entropy analysis of complex physiologic time seriesh. Physics Review Letters 89, 068102.Google Scholar
& (2007). An Introduction to the Theory of Point Processes: Vol. I, II, New York: Springer.
, & (1995). Relationships between short-term bloodpressure fluctuations and heart-rate variability in resting subjects: a spectral analysis approach. Medical & Biological Engineering & Computing 23, 352–358.Google Scholar
(2008). Arterial baroreflexes and cardiovascular modeling. Cardiovascular Engineering 8, 5–13.Google Scholar
, , & (2004). Dynamic analyses of neural encoding by point process adaptive filtering. Neural Computation 16, 971–998.Google Scholar
(2001). Adaptive Filter Theory, 4th edn, Englewood Cliffs, NS: Prentice Hall. & (1997). Regional Frequency Analysis: An Approach Based on L-moments, Cambridge: Cambridge University Press.
, , , , & (2007). A nonlinear model of cardiac autonomic control in obstructive sleep apnea syndrome. Annals of Biomedical Engineering 35, 1425–1443.Google Scholar
(1999). Physiological Control Systems: Analysis, Simulation, and Estimation, New York: Wiley-IEEE Press.
(1991). Parallel cascade identification and kernel estimation for nonlinear systems. Annals of Biomedical Engineering 19, 429–455.Google Scholar
, & (2001). A new algorithm for linear and nonlinear arma model parameter estimation using affine geometry. IEEE Transactions on Biomedical Engineering 48, 1116–1124.Google Scholar
(2002). Neural influences on cardiovascular variability: possibilities and pitfalls. American Journal of Physiology – Heart and Circulatory Physiology 282, 6–20.Google Scholar
(1993). Identification of nonlinear biological systems using Laguerre expansions of kernels. Annals of Biomedical Engineering 21, 573–589.Google Scholar
(2004). Nonlinear Dynamic Modeling of Physiological Systems, Chichester: Wiley.
, , , , & (2008). Brain correlates of autonomic modulation: combining heart rate variability with fMRI. Neuroimage 42(1), 169–177.Google Scholar
, & (2001). Dynamic modulation of baroreflex sensitivity in health and disease. Annals of New York Academy of Science 940, 469–487.Google Scholar
, , & (2009). Multimodal signal processing for the analysis of cardiovascular variability. Philosophical Transactions on Royal Society, Series A 367, 391–409.Google Scholar
, , , , & (2002). Quantifying the strength of the linear causal coupling in closed loop interacting cardiovascular variability signals. Biological Cybernetics 86, 241–251.Google Scholar
(1997). Introduction to Probability Models, 6th edn, London: Academic Press.
, , , , & (1991). Transfer function analysis of the circulation: unique insights into cardiovascular regulation. American Journal of Physiology – Heart and Circulatory Physiology 261, 1231–1245.Google Scholar
& (1994). Respiratory sinus arrhythmia. In & , eds, Vagal Control of the Heart: Experimental Basis and Clinical Implications, New York: Futura Publishing Inc.
(1980). The Volterra and Wiener Theories of Nonlinear Systems, Chichester: Wiley.
& (2003). Estimating a state-space model from point process observations. Neural Computation 15(5), 965–991.Google Scholar
(2003). Heart rate variability. American Journal of Physiology – Regulatory, Integrative and Comparative Physiology 285, 927–931.Google Scholar
, , , & (2003). 1/f scaling in heart rate requires antagonistic autonomic control. Physical Review E 70, 050901.Google Scholar
, & (2001). Identification of input output bilinear systems using cumulants. IEEE Transactions on Signal Processing 49, 2753–2761.Google Scholar
, & (2014b). Estimation of instantaneous complex dynamics through Lyapunov exponents: a study on heartbeat dynamics. PLoS ONE 9(8), e10562.Google Scholar
, , & (2013). Point-process nonlinear models with Laguerre and Volterra expansions: instantaneous assessment of heartbeat dynamics. IEEE Transactions on Signal Processing 61(11), 2914–2926.Google Scholar
, , & (2014a). Inhomogeneous point-process entropy: an instantaneous measure of complexity in discrete systems. Physical Review E 89, 052803.Google Scholar
, , , & (2009). Methods derived from nonlinear dynamics for analysing heart rate variability. Philosophical Transactions of the Royal Society A 367, 277–296.Google Scholar
(2007). The ARIMA and VARIMA Time Series: Their Modelings, Analyses and Applications, Ottawa: AuLac Technologies.
& (2003). Explicit least-squares methods. In Identification of Nonlinear Physiological Systems, Chickester: Wiley, pp. 169–206.
, & (2005). System identification: a multi-signal approach for probing neural cardiovascular regulation. Physiological Measurement 26, R41–R71.Google Scholar
, , , & (2004). Nonlinear analysis of separate contributions of autonomous nervous systems to heart rate variability using principal dynamic modes. IEEE Transactions on Biomedical Engineering 51, 255–262.Google Scholar
, , & (2007). Time-varying causal coherence function and its application to renal blood pressure and blood flow data. IEEE Transactions on Biomedical Engineering 54, 2142–2150.Google Scholar
, , , & (2005). Estimation of time-varying coherence function using time-varying transfer functionss. Annals of Biomedical Engineering 33, 1582–1594.Google Scholar