Closed-loop system identification of cardiovascular control mechanisms in diabetic autonomic neuropathy

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 92-95).by Ramadrishna Mukkamala.M.S

A forward model-based analysis of cardiovascular system identification methods

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 211-221).Cardiovascular system identification is a potentially powerful approach for intelligent patient monitoring of cardiovascular function. Rather than merely recording hemodynamic signals, the signals are mathematically analyzed so as to provide a dynamical characterization of the physiologic mechanisms responsible for generating them. The fundamental aim of this thesis is to develop and evaluate cardiovascular system identification methods based on a test bed of data generated from a forward model of the cardiovascular system whose dynamical properties are known. To this end, we developed a computer model of the human cardiovascular system which includes a lumped parameter model of the heart and circulation and a model of the short-term cardiovascular regulatory system continuously disturbed by resting physiologic perturbations. The short-term regulatory system consists of arterial and cardiopulmonary baroreflex systems and a direct neural coupling mechanism between respiration and heart rate. The resting physiologic perturbations include respiratory activity and stochastic disturbances to total peripheral resistance (TPR) and heart rate representing, for example, autoregulation of local vascular beds and higher brain center activity. We demonstrated that this model emulates experimental data in terms of steady-state pulsatility, limiting static behavior, and low frequency hemodynamic variability. We first evaluated the performance of a previously developed cardiovascular system identification method against the forward model.(cont.) The method involves the analysis of fluctuations in heart rate, arterial blood pressure (ABP), and instantaneous lung volume in order to characterize quantitatively important physiologic mechanisms including, for example, the heart rate baroreflex. From this analysis, we inferred that the cardiovascular system identification results derived from experimental data are likely to reflect the actual system dynamics of underlying physiologic mechanisms. We then introduced novel identification methods for quantifying TPR baroreflex dynamics from only fluctuations in cardiac output and ABP and for monitoring steady-state changes in TPR from only the ABP waveform. We demonstrated the efficacy f these identification methods with respect to forward model generated data and a preliminary set of experimental data. The results of this forward model-based analysis motivate the experimental validation of the cardiovascular system identification methods considered in this thesis.by Ramakrishna Mukkamala.Ph.D

Methods and apparatus for determining cardiac output

The present invention provides methods and apparatus for determining a dynamical property of the systemic or pulmonary arterial tree using long time scale information, i.e., information obtained from measurements over time scales greater than a single cardiac cycle. In one aspect, the invention provides a method and apparatus for monitoring cardiac output (CO) from a single blood pressure signal measurement obtained at any site in the systemic or pulmonary arterial tree or from any related measurement including, for example, fingertip photoplethysmography.According to the method the time constant of the arterial tree, defined to be the product of the total peripheral resistance (TPR) and the nearly constant arterial compliance, is determined by analyzing the long time scale variations (greater than a single cardiac cycle) in any of these blood pressure signals. Then, according to Ohm's law, a value proportional to CO may be determined from the ratio of the blood pressure signal to the estimated time constant. The proportional CO values derived from this method may be calibrated to absolute CO, if desired, with a single, absolute measure of CO (e.g., thermodilution). The present invention may be applied to invasive radial arterial blood pressure or pulmonary arterial blood pressure signals which are routinely measured in intensive care units and surgical suites or to noninvasively measured peripheral arterial blood pressure signals or related noninvasively measured signals in order to facilitate the clinical monitoring of CO as well as TPR

Cardiovascular Variability, Sociodemographics, and Biomarkers of Disease: The MIDUS Study

Introduction: Like heart rate, blood pressure (BP) is not steady but varies over intervals as long as months to as short as consecutive cardiac cycles. This blood pressure variability (BPV) consists of regularly occurring oscillations as well as less well-organized changes and typically is computed as the standard deviation of multiple clinic visit-to-visit (VVV-BP) measures or from 24-h ambulatory BP recordings (ABPV). BP also varies on a beat-to-beat basis, quantified by methods that parse variation into discrete bins, e.g., low frequency (0.04–0.15 Hz, LF). However, beat-to-beat BPV requires continuous recordings that are not easily acquired. As a result, we know little about the relationship between LF-BPV and basic sociodemographic characteristics such as age, sex, and race and clinical conditions. Methods: We computed LF-BPV during an 11-min resting period in 2,118 participants in the Midlife in the US (MIDUS) study. Results: LF-BPV was negatively associated with age, greater in men than women, and unrelated to race or socioeconomic status. It was greater in participants with hypertension but unrelated to hyperlipidemia, hypertriglyceridemia, diabetes, elevated CRP, or obesity. LF-diastolic BPV (DBPV), but not-systolic BPV (SBPV), was negatively correlated with IL-6 and s-ICAM and positively correlated with urinary epinephrine and cortisol. Finally, LF-DBPV was negatively associated with mortality, an effect was rendered nonsignificant by adjustment by age but not other sociodemographic characteristics. Discussion: These findings, the first from a large, national sample, suggest that LF-BPV differs significantly from VVV-BP and ABPV. Confirming its relationship to sociodemographic risk factors and clinical outcomes requires further study with large and representative samples

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i [...

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i [...