Aerospace medicine and biology: A continuing bibliography with indexes, supplement 203

This bibliography lists 150 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1980

Recursive estimation of Systemic Vascular Resistance using measurements from a left ventricular assist device

Cardiovascular disease is the leading cause of deaths worldwide, and one of the ways to treat patients with congestive heart failure is to perform a heart transplant. As the demand for this procedure rises, the disproportionate availability of suitable donors needs to be countered with ways to care and sustain patients who are waiting for a transplant. In this regard, the use of left ventricular assist devices (LVAD) has increased. The research conducted in this Thesis is primarily concerned with the TORVAD [superscript TM] (Windmill Cardiovascular Systems In., Austin , TX), a rotary blood pump type LVAD. The load faced by the left ventricle during ejection of blood is termed as Systemic Vascular Resistance (SVR), and is an important parameter that can indicate cardiovascular health. Abnormalities in SVR have been found to be a good indicator of hypertension, heart failure, shock and sepsis. A consistently low SVR can even be a predictor of mortality. The goal of this Thesis is to investigate ways of recursively estimating SVR in a patient, by using measurements that the TORVAD [superscript TM] provides. The Extended Kalman Filter is used to develop an estimation algorithm based on a reduced order model of the cardiovascular system. The estimation accuracy of the algorithm is tested by generating data through simulations of a computational model of the cardiovascular system, and by collecting measurements from the TORVAD [superscript TM] while it operates in a mock circulation loop. The algorithm is found to estimate SVR satisfactorily using the available measurements, and is robust to different initial conditions.Mechanical Engineerin

On adaptive control and particle filtering in the automatic administration of medicinal drugs

Automatic feedback methodologies for the administration of medicinal drugs offer undisputed potential benefits in terms of cost reduction and improved clinical outcomes. However, despite several decades of research, the ultimate safety of many--it would be fair to say most--closed-loop drug delivery approaches remains under question and manual methods based on clinicians' expertise are still dominant in clinical practice. Key challenges to the design of control systems for these applications include uncertainty in pharmacological models, as well as intra- and interpatient variability in the response to drug administration. Pharmacological systems may feature nonlinearities, time delays, time-varying parameters and non-Gaussian stochastic processes. This dissertation investigates a novel multi-controller adaptive control strategy capable of delivering safe control for closed-loop drug delivery applications without impairing clinicians' ability to make an expert assessment of a clinical situation. Our new feedback control approach, which we have named Robust Adaptive Control with Particle Filtering (RAC-PF), estimates a patient's individual response characteristic in real-time through particle filtering and uses the Bayesian inference result to select the most suitable controller for closed-loop operation from a bank of candidate controllers designed using the robust methodology of mu-synthesis. The work is presented as four distinct pieces of research. We first apply the existing approach of Robust Multiple-Model Adaptive Control (RMMAC), which features robust controllers and Kalman filter estimators, to the case-study of administration of the vasodepressor drug sodium nitroprusside and examine benefits and drawbacks. We then consider particle filtering as an alternative to Kalman filter-based methods for the real-time estimation of pharmacological dose-response, and apply this to the nonlinear pharmacokinetic-pharmacodynamic model of the anaesthetic drug propofol. We ultimately combine particle filters and robust controllers to create RAC-PF, and test our novel approach first in a proof-of-concept design and finally in the case of sodium nitroprusside. The results presented in the dissertation are based on computational studies, including extensive Monte-Carlo simulation campaigns. Our findings of improved parameter estimates from noisy observations support the use of particle filtering as a viable tool for real-time Bayesian inference in pharmacological system identification. The potential of the RAC-PF approach as an extension of RMMAC for closed-loop control of a broader class of systems is also clearly highlighted, with the proposed new approach delivering safe control of acute hypertension through sodium nitroprusside infusion when applied to a very general population response model. All approaches presented are generalisable and may be readily adapted to other drug delivery instances

Physiology-based Mathematical Models for the Intensive Care Unit: Application to Mechanical Ventilation

This work takes us a step closer to realizing personalized medicine, complementing empirical and heuristic way in which clinicians typically work. This thesis presents mechanistic models of physiology. These models, given continuous signals from a patient, can be fine-tuned via parameter estimation methods so that the model's outputs match the patient's. We thus obtain a virtual patient mimicking the patient at hand. Therapeutic scenarios can then be applied and optimal diagnosis and therapy can thus be attained. As such, personalized medicine can then be achieved without resorting to costly genetics. In particular we have developed a novel comprehensive mathematical model of the cardiopulmonary system that includes cardiovascular circulation, respiratory mechanics, tissue and alveolar gas exchange, as well as short-term neural control. Validity of the model was proven by the excellent agreement with real patient data, under normo-physiological as well as hypercapnic and hypoxic conditions, taken from literature. As a concrete example, a submodel of the lung mechanics was fine-tuned using real patient data and personalized respiratory parameters (resistance, R_rs, and compliance, C_rs) were estimated continually. This allows us to compute the patient's effort (Work of Breathing), continuously and more importantly noninvasively. Finally, the use of Bayesian estimation techniques, which allow incorporation of population studies and prior information about model's parameters, was proposed in the contest of patient-specific physiological models. A Bayesian Maximum a Posteriori Probability (MAP) estimator was implemented and applied to a case-study of respiratory mechanics. Its superiority against the classical Least Squares method was proven in data-poor conditions using both simulated and real animal data. This thesis can serve as a platform for a plethora of applications for cardiopulmonary personalized medicine

Extraction and Detection of Fetal Electrocardiograms from Abdominal Recordings

The non-invasive fetal ECG (NIFECG), derived from abdominal surface electrodes, offers novel diagnostic possibilities for prenatal medicine. Despite its straightforward applicability, NIFECG signals are usually corrupted by many interfering sources. Most significantly, by the maternal ECG (MECG), whose amplitude usually exceeds that of the fetal ECG (FECG) by multiple times. The presence of additional noise sources (e.g. muscular/uterine noise, electrode motion, etc.) further affects the signal-to-noise ratio (SNR) of the FECG. These interfering sources, which typically show a strong non-stationary behavior, render the FECG extraction and fetal QRS (FQRS) detection demanding signal processing tasks. In this thesis, several of the challenges regarding NIFECG signal analysis were addressed. In order to improve NIFECG extraction, the dynamic model of a Kalman filter approach was extended, thus, providing a more adequate representation of the mixture of FECG, MECG, and noise. In addition, aiming at the FECG signal quality assessment, novel metrics were proposed and evaluated. Further, these quality metrics were applied in improving FQRS detection and fetal heart rate estimation based on an innovative evolutionary algorithm and Kalman filtering signal fusion, respectively. The elaborated methods were characterized in depth using both simulated and clinical data, produced throughout this thesis. To stress-test extraction algorithms under ideal circumstances, a comprehensive benchmark protocol was created and contributed to an extensively improved NIFECG simulation toolbox. The developed toolbox and a large simulated dataset were released under an open-source license, allowing researchers to compare results in a reproducible manner. Furthermore, to validate the developed approaches under more realistic and challenging situations, a clinical trial was performed in collaboration with the University Hospital of Leipzig. Aside from serving as a test set for the developed algorithms, the clinical trial enabled an exploratory research. This enables a better understanding about the pathophysiological variables and measurement setup configurations that lead to changes in the abdominal signal's SNR. With such broad scope, this dissertation addresses many of the current aspects of NIFECG analysis and provides future suggestions to establish NIFECG in clinical settings.:Abstract Acknowledgment Contents List of Figures List of Tables List of Abbreviations List of Symbols (1)Introduction 1.1)Background and Motivation 1.2)Aim of this Work 1.3)Dissertation Outline 1.4)Collaborators and Conflicts of Interest (2)Clinical Background 2.1)Physiology 2.1.1)Changes in the maternal circulatory system 2.1.2)Intrauterine structures and feto-maternal connection 2.1.3)Fetal growth and presentation 2.1.4)Fetal circulatory system 2.1.5)Fetal autonomic nervous system 2.1.6)Fetal heart activity and underlying factors 2.2)Pathology 2.2.1)Premature rupture of membrane 2.2.2)Intrauterine growth restriction 2.2.3)Fetal anemia 2.3)Interpretation of Fetal Heart Activity 2.3.1)Summary of clinical studies on FHR/FHRV 2.3.2)Summary of studies on heart conduction 2.4)Chapter Summary (3)Technical State of the Art 3.1)Prenatal Diagnostic and Measuring Technique 3.1.1)Fetal heart monitoring 3.1.2)Related metrics 3.2)Non-Invasive Fetal ECG Acquisition 3.2.1)Overview 3.2.2)Commercial equipment 3.2.3)Electrode configurations 3.2.4)Available NIFECG databases 3.2.5)Validity and usability of the non-invasive fetal ECG 3.3)Non-Invasive Fetal ECG Extraction Methods 3.3.1)Overview on the non-invasive fetal ECG extraction methods 3.3.2)Kalman filtering basics 3.3.3)Nonlinear Kalman filtering 3.3.4)Extended Kalman filter for FECG estimation 3.4)Fetal QRS Detection 3.4.1)Merging multichannel fetal QRS detections 3.4.2)Detection performance 3.5)Fetal Heart Rate Estimation 3.5.1)Preprocessing the fetal heart rate 3.5.2)Fetal heart rate statistics 3.6)Fetal ECG Morphological Analysis 3.7)Problem Description 3.8)Chapter Summary (4)Novel Approaches for Fetal ECG Analysis 4.1)Preliminary Considerations 4.2)Fetal ECG Extraction by means of Kalman Filtering 4.2.1)Optimized Gaussian approximation 4.2.2)Time-varying covariance matrices 4.2.3)Extended Kalman filter with unknown inputs 4.2.4)Filter calibration 4.3)Accurate Fetal QRS and Heart Rate Detection 4.3.1)Multichannel evolutionary QRS correction 4.3.2)Multichannel fetal heart rate estimation using Kalman filters 4.4)Chapter Summary (5)Data Material 5.1)Simulated Data 5.1.1)The FECG Synthetic Generator (FECGSYN) 5.1.2)The FECG Synthetic Database (FECGSYNDB) 5.2)Clinical Data 5.2.1)Clinical NIFECG recording 5.2.2)Scope and limitations of this study 5.2.3)Data annotation: signal quality and fetal amplitude 5.2.4)Data annotation: fetal QRS annotation 5.3)Chapter Summary (6)Results for Data Analysis 6.1)Simulated Data 6.1.1)Fetal QRS detection 6.1.2)Morphological analysis 6.2)Own Clinical Data 6.2.1)FQRS correction using the evolutionary algorithm 6.2.2)FHR correction by means of Kalman filtering (7)Discussion and Prospective 7.1)Data Availability 7.1.1)New measurement protocol 7.2)Signal Quality 7.3)Extraction Methods 7.4)FQRS and FHR Correction Algorithms (8)Conclusion References (A)Appendix A - Signal Quality Annotation (B)Appendix B - Fetal QRS Annotation (C)Appendix C - Data Recording GU

Modelling, Optimisation and Explicit Model Predictive Control of Anaesthesia Drug Delivery Systems

The contributions of this thesis are organised in two parts. Part I presents a mathematical model for drug distribution and drug effect of volatile anaesthesia. Part II presents model predictive control strategies for depth of anaesthesia control based on the derived model. Closed-loop model predictive control strategies for anaesthesia are aiming to improve patient's safety and to fine-tune drug delivery, routinely performed by the anaesthetist. The framework presented in this thesis highlights the advantages of extensive modelling and model analysis, which are contributing to a detailed understanding of the system, when aiming for the optimal control of such system. As part of the presented framework, the model uncertainty originated from patient-variability is analysed and the designed control strategy is tested against the identified uncertainty. An individualised physiologically based model of drug distribution and uptake, pharmacokinetics, and drug effect, pharmacodynamics, of volatile anaesthesia is presented, where the pharmacokinetic model is adjusted to the weight, height, gender and age of the patient. The pharmacodynamic model links the hypnotic depth measured by the Bispectral index (BIS), to the arterial concentration by an artificial effect site compartment and the Hill equation. The individualised pharmacokinetic and pharmacodynamic variables and parameters are analysed with respect to their influence on the measurable outputs, the end-tidal concentration and the BIS. The validation of the model, performed with clinical data for isoflurane and desflurane based anaesthesia, shows a good prediction of the drug uptake, while the pharmacodynamic parameters are individually estimated for each patient. The derived control design consists of a linear multi-parametric model predictive controller and a state estimator. The non-measurable tissue and blood concentrations are estimated based on the end-tidal concentration of the volatile anaesthetic. The designed controller adapts to the individual patient's dynamics based on measured data. In an alternative approach, the individual patient's sensitivity is estimated on-line by solving a least squares parameter estimation problem.Open Acces

Investigation of the performance of an automatic arterial oxygen controller

Premature infants often require respiratory support with a varying concentration of the fraction of inspired oxygen (FiO2) to keep the arterial oxygen saturation (SpO2) within the desired range to avoid both hypoxemia and hyperoxemia. Currently, manual adjustment of FiO2 is the common practice in neonatal intensive care units (NICUs). The automation of this adjustment is a topic of interest. The research team, at University of Missouri-Columbia (UMC), has developed a novel automatic arterial oxygen saturation controller. In this study, a systematic approach has been developed to investigate both non-clinical and clinical performance of this device. The non-clinical investigation of the performance was performed using a neonatal respiratory model (hardware-in-the-loop test). A factorial experimental design was utilized to generate challenging model responses of SpO2, which were addressed by the controllers. With this study, we demonstrate the stability and ability of the adaptive PI-controller to improve oxygen saturation control over manual control by increasing the proportion of time where SpO2 of the neonatal respiratory model was within the desired range and by minimizing the variability of the SpO2. In addition, the controller ability to significantly reduce the number of hypoxemic events of the neonatal respiratory model was reported. Results of this investigation show the competence of the controller estimation system for estimating neonatal respiratory model parameters while the adaptive PI-controller was in use. Also, the functionality of the controller with no mechanical or communication failure was validated non-clinically before heading forward to the clinical trial. The clinical investigation of the performance was performed by conducting a clinical trial at the NICU of the MU Women's and Children's Hospital. The crossover design was used for the clinical trial to allow within-subject comparison and to eliminate interpatient variability. Two human subjects, with two different target ranges of SpO2, were enrolled in the study. The adaptive automatic PI-controller shows clinical feasibility to improve the maintenance of SpO2 within the intended range. With this study, we demonstrate the potential of the automatic controller to minimize the variability of SpO2. In addition, the controller shows the ability to reduce the bradycardia and the hypoxemia. Moreover, the hardware and software of the controller show an ability to transition from manual to automatic mode, and vice versa with no pronounced “bump” or step variation in the control signal, and stability and performance were not adversely affected during the transitions.Includes bibliographical reference

Polynomial system identification modeling and adaptive model predictive control of arterial oxygen saturation in premature infants

The automation of the regulation of the fraction of inspired oxygen (FiO2) in neonatal mechanical ventilation to treat respiratory distress syndrome has proven challenging due to competing objectives: maintaining arterial oxygen saturation levels (SpO2) while simultaneously not inducing complications such as retrolental fibroplasia. Historically, models of the dynamics of the neonatal respiratory system were first order transfer function approximations. This work used higher order polynomial system identification methods with the model structures of autoregressive with exogenous inputs (ARX) and Box-Jenkins (BJ) models to investigate possible improved modeling of the dynamic relationship between the FiO2, Heart Rate (HR), and Respiratory Rate (RR) to the SpO2. Through a parameter sweep of different of polynomial orders and sampling delays, 3,456 ARX models and 13,176 BJ models were created, with four being selected for comparison based upon modeling performance metrics. From these best performing models, it was concluded that the FiO2 relationship to SpO2 could still be adequately approximated by a first order transfer function model with delay. The disturbance HR, RR, and the unmodeled dynamics did require higher order approximations. It was also shown that selecting a model based off the Akaike's Information Criterion was preferred in picking a model from a collection of identified models. With a singular winning model from the four best performing models, an adaptive model predictive controller (AMPC) was designed to adhere to clinical best practices to regulate the SpO2. Through a recursive polynomial model estimator (RPME), an ARX approximation of the unknown model's dynamics for the FiO2, HR, and RR relationship to the SpO2 could be used to update the internal model of the AMPC. Through this online model estimation, the AMPC could successfully feedforward reject the HR and RR disturbances improving the simulated time within the SpO2 target limits, 67.8% of simulation time, to a baseline PI controller's 56.6%, in periodic desaturation simulations.Includes bibliographical references (pages 63-65)

Understanding Peripheral Blood Pressure Signals: A Statistical Learning Approach

Proper estimation of body fluid status for human or animal subjects has always been a challenging problem. Accurate and timely estimate of body fluid can prevent life threatening conditions under trauma and severe dehydration. The main objective of this research is the estimation, classification and detection of dehydration in human and animal subjects using peripheral blood pressure (PBP) signals. Peripheral venous pressure (PVP) and peripheral arterial pressure (PAP) signals have been investigated in this research. Both PVP and PAP signals are PBP signals. A dataset of PVP signals was collected using standard peripheral intravenous catheters from human subjects suffering from hypertrophic pyloric stenosis. Using this dataset, we successfully classified dehydrated subjects from hydrated subjects using regularized logistic regression on frequency domain data of the PVP signals. During the data acquisition process, the PVP signals was corrupted by noise and blood clot. So, we developed an unsupervised anomaly detection algorithm for PVP signals using hidden Markov model and Kalman filter. This anomaly detection algorithm removed the human bias in data-preprocessing. Another dataset of PAP and PVP signals was collected from pigs under anesthesia using the Millar catheter. We proposed a integral pulse frequency modulation (IPFM) based signal model for both PAP and PVP signals. The proposed model-synthesized signal is highly correlated with the experimental data. The model-synthesized signals also performs similar to experimental signals under classification tasks. We also examine the model estimated parameters both qualitatively and quantitatively. This model can also quantify the effect of respiratory rate on heart rate variability. Increasing doses of anesthesia has similar effect of getting hydrated from dehydration

Understanding Peripheral Blood Pressure Signals: A Statistical Learning Approach

Proper estimation of body fluid status for human or animal subjects has always been a challenging problem. Accurate and timely estimate of body fluid can prevent life threatening conditions under trauma and severe dehydration. The main objective of this research is the estimation, classification and detection of dehydration in human and animal subjects using peripheral blood pressure (PBP) signals. Peripheral venous pressure (PVP) and peripheral arterial pressure (PAP) signals have been investigated in this research. Both PVP and PAP signals are PBP signals. A dataset of PVP signals was collected using standard peripheral intravenous catheters from human subjects suffering from hypertrophic pyloric stenosis. Using this dataset, we successfully classified dehydrated subjects from hydrated subjects using regularized logistic regression on frequency domain data of the PVP signals. During the data acquisition process, the PVP signals was corrupted by noise and blood clot. So, we developed an unsupervised anomaly detection algorithm for PVP signals using hidden Markov model and Kalman filter. This anomaly detection algorithm removed the human bias in data-preprocessing. Another dataset of PAP and PVP signals was collected from pigs under anesthesia using the Millar catheter. We proposed a integral pulse frequency modulation (IPFM) based signal model for both PAP and PVP signals. The proposed model-synthesized signal is highly correlated with the experimental data. The model-synthesized signals also performs similar to experimental signals under classification tasks. We also examine the model estimated parameters both qualitatively and quantitatively. This model can also quantify the effect of respiratory rate on heart rate variability. Increasing doses of anesthesia has similar effect of getting hydrated from dehydration