Identifying evolving multivariate dynamics in individual and cohort time series, with application to physiological control systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 117-125).Physiological control systems involve multiple interacting variables operating in feedback loops that enhance an organism's ability to self-regulate and respond to internal and external disturbances. The resulting multivariate time-series often exhibit rich dynamical patterns, which are altered under pathological conditions. However, model identification for physiological systems is complicated by measurement artifacts and changes between operating regimes. The overall aim of this thesis is to develop and validate computational tools for identification and analysis of structured multivariate models of physiological dynamics in individual and cohort time-series. We first address the identification and stability of the respiratory chemoreflex system, which is key to the pathogenesis of sleep-induced periodic breathing and apnea. Using data from both an animal model of periodic breathing, as well as human recordings from clinical sleep studies, we demonstrate that model-based analysis of the interactions involved in spontaneous breathing can characterize the dynamics of the respiratory control system, and provide a useful tool for quantifying the contribution of various dynamic factors to ventilatory instability. The techniques have suggested novel approaches to titration of combination therapies, and clinical evaluations are now underway. We then study shared multivariate dynamics in physiological cohort time-series, assuming that the time-series are generated by switching among a finite collection of physiologically constrained dynamical models. Patients whose time-series exhibit similar dynamics may be grouped for monitoring and outcome prediction. We develop a novel parallelizable machine-learning algorithm for outcome-discriminative identification of the switching dynamics, using a probabilistic dynamic Bayesian network to initialize a deterministic neural network classifier. In validation studies involving simulated data and human laboratory recordings, the new technique significantly outperforms the standard expectation-maximization approach for identification of switching dynamics. In a clinical application, we show the prognostic value of assessing evolving dynamics in blood pressure time-series to predict mortality in a cohort of intensive care unit patients. A better understanding of the dynamics of physiological systems in both health and disease may enable clinicians to direct therapeutic interventions targeted to specific underlying mechanisms. The techniques developed in this thesis are general, and can be extended to other domains involving multi-dimensional cohort time-series.by Shamim Nemati.Ph.D

Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series

Background: The detection of change in magnitude of directional coupling between two non-linear time series is a common subject of interest in the biomedical domain, including studies involving the respiratory chemoreflex system. Although transfer entropy is a useful tool in this avenue, no study to date has investigated how different transfer entropy estimation methods perform in typical biomedical applications featuring small sample size and presence of outliers. Methods: With respect to detection of increased coupling strength, we compared three transfer entropy estimation techniques using both simulated time series and respiratory recordings from lambs. The following estimation methods were analyzed: fixed-binning with ranking, kernel density estimation (KDE), and the Darbellay-Vajda (D-V) adaptive partitioning algorithm extended to three dimensions. In the simulated experiment, sample size was varied from 50 to 200, while coupling strength was increased. In order to introduce outliers, the heavy-tailed Laplace distribution was utilized. In the lamb experiment, the objective was to detect increased respiratoryrelated chemosensitivity to O[subscript 2] and CO[subscript 2] induced by a drug, domperidone. Specifically, the separate influence of end-tidal PO[subscript 2] and PCO[subscript 2] on minute ventilation ([dot over V][subscript E]) before and after administration of domperidone was analyzed. Results: In the simulation, KDE detected increased coupling strength at the lowest SNR among the three methods. In the lamb experiment, D-V partitioning resulted in the statistically strongest increase in transfer entropy post-domperidone for PO2 → [dot over V][subscript E]. In addition, D-V partitioning was the only method that could detect an increase in transfer entropy for PCO[subscript 2] → [dot over V][subscript E], in agreement with experimental findings. Conclusions: Transfer entropy is capable of detecting directional coupling changes in non-linear biomedical time series analysis featuring a small number of observations and presence of outliers. The results of this study suggest that fixed-binning, even with ranking, is too primitive, and although there is no clear winner between KDE and D-V partitioning, the reader should note that KDE requires more computational time and extensive parameter selection than D-V partitioning. We hope this study provides a guideline for selection of an appropriate transfer entropy estimation method.National Institutes of Health (U.S.) (Grant R01-EB001659)National Institutes of Health (U.S.) (Grant R01- HL73146)National Institutes of Health (U.S.) (Grant HL085188-01A2)National Institutes of Health (U.S.) (Grant HL090897-01A2)National Institutes of Health (U.S.) (Grant K24 HL093218-01A1)National Institutes of Health (U.S.) (Cooperative Agreement U01-EB-008577)National Institutes of Health (U.S.) (Training Grant T32-HL07901))American Heart Association (Grant 0840159N

An artificial vector model for generating abnormal electrocardiographic rhythms

We present generalizations of our previously published artificial models for generating multi-channel ECG to provide simulations of abnormal cardiac rhythms. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are specified either a perturbations to the normal dipole or as new dipole trajectories. Switching between normal and abnormal beat types is achieved using a first-order Markov chain. Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes from beat-to-beat are incorporated by varying the angular frequency of the dipole as a function of the inter-beat (RR) interval. The RR interval time series is generated using our previously described model whereby time- and frequency-domain heart rate (HR) and heart rate variability characteristics can be specified. QT-HR hysteresis is simulated by coupling by Gaussian kernels associated with the T-wave in the model with a nonlinear factor related to the local HR (determined from the last n RR intervals). Morphology changes due to respiratory frequency. We demonstrate an example of the use of this model by switching HR-dependent T-wave alternans (TWA) with and without phase-switching due to ectopy. Application of our model also reveals previously unreported effects of common TWA estimation methods