Heart Rate Variability: A possible machine learning biomarker for mechanical circulatory device complications and heart recovery

Cardiovascular disease continues to be the number one cause of death in the United States, with heart failure patients expected to increase to \u3e8 million by 2030. Mechanical circulatory support (MCS) devices are now better able to manage acute and chronic heart failure refractory to medical therapy, both as bridge to transplant or as bridge to destination. Despite significant advances in MCS device design and surgical implantation technique, it remains difficult to predict response to device therapy. Heart rate variability (HRV), measuring the variation in time interval between adjacent heartbeats, is an objective device diagnostic regularly recorded by various MCS devices that has been shown to have significant prognostic value for both sudden cardiac death as well as all-cause mortality in congestive heart failure (CHF) patients. Limited studies have examined HRV indices as promising risk factors and predictors of complication and recovery from left ventricular assist device therapy in end-stage CHF patients. If paired with new advances in machine learning utilization in medicine, HRV represents a potential dynamic biomarker for monitoring and predicting patient status as more patients enter the mechanotrope era of MCS devices for destination therapy

Implementation of cyclic exercise protocol on two study groups - AIDS and insomnia

Traditional exercise regimens are based on maintaining a prolonged increase in heart rate, followed by a single recovery period. The Cyclic Exercise Protocol is a novel protocol that is designed to create a series of parabolic waves of exercise and recovery. This study involves the implementation of this exercise protocol on two study groups namely AIDS and Insomnia. This exercise protocol involves short bursts of exercise lasting for 60 seconds or less followed by a period of complete aerobic recovery. The underlying principle of this exercise protocol is that rest, recovery and the body\u27s natural rhythm are important to fitness and conditioning. The study involves the analysis of heart rate during cycles, focused breathing - breathing at a specific rate of 12 breaths per minute and circadian data - 24 hour biological rhythm of our body, for the AIDS population and only the heart rate data during cycles in case of Insomnia using Mathematica and Lab View. The subject populations as well as the physiological signals utilized in this study were obtained from the Philadelphia FIGHT Institute for AIDS and Harvard Medical School for Insomnia. The parameters obtained during analysis of data from both study groups were statistically analyzed. There were significant results for the slope base parameter in case of the AIDS study and deep breath and downslope parameters in case of the Insomnia study. Therefore the cycles protocol with minimum exertion confers maximum benefits to our body

Analysis of acceleration and deceleration during cyclic exercise

The Cyclic Exercise Protocol is a novel protocol that is designed to create a series of cyclic waves of exercise and recovery. This exercise protocol involves short bursts of exercise lasting for 60 seconds or less followed by a period of complete cardio recovery. This study involves the analysis of the acceleration and the deceleration regions of the cyclic exercise sessions. The objective of this study was to develop an algorithm to quantitatively analyze the acceleration and the deceleration regions of cyclic exercise sessions. The acceleration and the deceleration regions of cycles were split into three segments and each segment was fit with mathematical curves and the variation of data from the fit was calculated. The cycles data was analyzed using Mathematica version 4.1. The subjects for this study were healthy volunteers. The parameters extracted included the time constants and slopes from the mathematical fits and the mean square errors. The mean square error values obtained were less than 14.8 (±3.5% error in the variation of the heart rate), showing that the algorithm is creating a proper mathematical fit to the different regions of acceleration and deceleration

Evidence toward the potential absence of relationship between temporal and spatial heartbeats perception

Many interoceptive tasks (i.e. measuring the sensitivity to bodily signals) are based upon heartbeats perception. However, the temporal perception of heartbeats—when heartbeats are felt—varies among individuals. Moreover, the spatial perception of heartbeats—where on the body heartbeats are felt—has not been characterized in relation to temporal. This study used a multi-interval heartbeat discrimination task in which participants judged the timing of their own heartbeats in relation to external tones. The perception of heartbeats in both time and spatial domains, and relationship between these domains was investigated. Heartbeat perception occurred on average ~ 250 ms after the ECG R-wave, most frequently sampled from the left part of the chest. Participants’ confidence in discriminating the timing of heartbeats from external tones was maximal at 0 ms (tone played at R-wave). Higher confidence was related to reduced dispersion of sampling locations, but Bayesian statistics indicated the absence of relationship between temporal and spatial heartbeats perception. Finally, the spatial precision of heartbeat perception was related to state-anxiety scores, yet largely independent of cardiovascular parameters. This investigation of heartbeat perception provides fresh insights concerning interoceptive signals that contribute to emotion, cognition and behaviour

Investigation of the relevance of heart rate variability changes after heart transplantation

Heart transplantation has become an established treatment for end-stage heart disease. However, the shortage of donor organs is a major problem and long-term results are limited by allograft rejection. Heart rate variability (HRV) has emerged as a popular noninvasive research tool in cardiology. Analysis of HRV is regarded as a valid technique to assess the sympathovagal balance of the heart. The primary goal of this study was to investigate the relevance of heart rate variability changes after heart transplantation. It was found that spectral analysis of HRV is useful in detecting rejection episodes. Heart transplantation leaves the donor heart denervated. Spectral analysis of HRV was found appropriate to detect functional autonomous reinnervation. Extensive literature review was done to validate the findings. The paper is divided into two parts. The first part of the paper deals mainly with the techniques and current status of heart transplantation. The second part, deals with the relevance of heart rate variability and reinnervation after heart transplantation. The results of the study suggest that heart rate variability analysis is a valuable tool in assessing the cardiovascular status after heart transplantation

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 183

This bibliography lists 273 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1978

Counterpulsation cardiac assist device controller defection filter simulation and canine experiments

Electronic control systems for counterpulsation Cardiac Assist Devices (CADs) are an essential part of cardiac assistance. Synchronization of the counterpulsation CAD controller with the cardiac cycle is critical to the efficacy of the CAD. The robustness of counterpulsation CAD controllers varies with the ability of the CAD controller to properly trigger on aortic pressure (Pa) and electrocardiogram (ECG) signals for sinusoid rhythms, non-sinusoid rhythms and non-ideal signals resulting from surgical intervention. An analog-to-digital converter and digital-to-analog converter based CAD controller development platform was devised on a 33Mhz PC-AT. Counterpulsation Pa systolic rise and dicrotic notch detectors were demonstrated with a 15cc pediatric Intraaortic Balloon (IAB) and 50cc Extraaortic Counterpulsation Device (EACD) CADs using mongrel canine experimental models in which biological variation due to changing heart rate and arrhythmia as well as surgical interference due to mechanical ventilation, electrocautery, signal attenuation and random noise was present. The robust Pa triggering algorithm was based on a derivative comparator riding clipper algorithm for the Pa-based controller. In order to empirically determine the robustness of the Pa triggering algorithms, a simulation platform, Pa trace model, and Pa trace artifact and physiological variation models were devised. Each set of simulation experiments utilized a different Pa trace artifact or physiological variation model to determine the capability of the Pa trigger algorithm to withstand the effects of the Pa detection impediments while maintaining 100% accuracy of the dicrotic notch detection. Multiple simulation experiments were conducted in which the same nominally adjusted interference was increased to benchmark the immunity threshold of the dicrotic notch detector. Biological variation and deviations in Pa artifacts due to clinical conditions experienced in cardiothoracic surgery were investigated. Pa triggering was unhindered by biological variation of a Pa trace with a 3 mmHg dicrotic notch deflection along with a Pa trace with no dicrotic notch deflection present. Pa triggering was unhindered by heart rate variability ranging from 60 to 80 bpm due to respiration. Pa triggering was unhindered by clinical conditions including 40 mmHg changes in the Pa baseline modeling mechanical ventilation, aortic trace attenuation modeling variations in pressure transducer positioning and blood coagulation on the pressure catheter tip ranging from 100% to 200% of the Pa trace amplitude every four seconds, uniformly distributed noise with a mean of 0.5mmHg and standard deviation of 0.289mmHg and Gaussian distributed noise with a zero mean and standard deviation of 0.6nunHg. The results of the simulation experiments performed quantified the robustness of the Pa detection algorithm. Development of a fault tolerant counterpulsation CAD control system required the development of a robust ECG triggering algorithm to operate in tandem with the Pa triggering algorithm. An ECG detector was developed to provide robust control for a range of ECG traces due to biological variation and signal interference. The ECG R-wave detection algorithm is based on a modified version of the Washington University QRS-complex DD/1 algorithm (Detection and Delineation 1) which uses the associated AZTEC (Amplitude Zero Threshold Epic Coding) preprocessing algorithm and provides accurate ECG-based CAD control R-wave detection for 96.56% of the R-waves stored within the MIT/BIH ECG Arrhythmia database with a maximum detection delay of 8 milliseconds. Further IAB experiments performed with mongrel canine experimental models demonstrated that the systolic time interval to heart rate relationship existing in humans (essential to human patient CAD control inflation prediction) is not prevalent in canine mongrels particularly when treated with beta-blockers. In order to execute both Pa and ECG C software detection algorithms for a fault tolerant counterpulsation CAD controller, investigation into the communications throughput of a quad-transputer board was performed. Development of streamlined communication primitives led to a communication processor utilization of 8.3%, deemed efficient enough for fault tolerant multiprocessor CAD control implementation

Enabling Wearable Hemodynamic Monitoring Using Multimodal Cardiomechanical Sensing Systems

Hemodynamic parameters such as blood pressure and stroke volume are instrumental to understanding the pathogenesis of cardiovascular disease. Unfortunately, the monitoring of these hemodynamic parameters is still limited to in-clinic measurements and cumbersome hardware precludes convenient, ubiquitous use. To address this burden, in this work, we explore seismocardiogram-based wearable multimodal sensing techniques to estimate blood pressure and stroke volume. First, the performance of a multimodal, wrist-worn device capable of obtaining noninvasive pulse transit time measurements is used to estimate blood pressure in an unsupervised, at-home setting. Second, the feasibility of this wrist-worn device is comprehensively evaluated in a diverse and medically underserved population over the course of several perturbations used to modulate blood pressure through different pathways. Finally, the ability of wearable signals—acquired from a custom chest-worn biosensor—to noninvasively quantify stroke volume in patients with congenital heart disease is examined in a hospital setting. Collectively, this work demonstrates the advancements necessary towards enabling noninvasive, longitudinal, and accurate measurements of these hemodynamic parameters in remote settings, which offers to improve health equity and disease monitoring in low-resource settings.Ph.D

Central and peripheral autonomic influences : analysis of cardio-pulmonary dynamics using novel wavelet statistical methods

The development and implementation of novel signal processing techniques, particularly with regard to applications in the clinical environment, is critical to bringing computer-aided diagnoses of disease to reality. One of the most confounding factors in the field of cardiac autonomic response (CAR) research is the influence of the coupling of respiratory oscillations with cardiac oscillations. This research had three objectives. The first was the assessment of central autonomic influence over heart rate oscillations when the pulmonary system is damaged. The second was to assess the link between peripheral and central autonomic control schema by evaluating the heart rate variability (HRV) of people who were able or unable to adapt to the use of integrated lenses for vision, specifically acconrrmodation, correction (adaptive and non-adaptive presbyopes). The third objective was the development of a wavelet-based toolset by which the first two objectives could be achieved. The first tool is a wavelet based entropy measure that quantifies the level of information by assessing not only the entropy levels, but also the distribution of the entropy across frequency bands. The second tool is a wavelet source separation (WayS) method used to separate the respiratory component from the cardiac component, thereby allowing for analysis of the dynamics of the cardiac signal without the confounding influence of the respiratory signal that occurs when the body is perturbed. With regard to hypothesis one, the entropy method was used to separate the COPD study populations with 93% classification accuracy at rest, and with 100% accuracy during exercise. Changes in COPD and control autonomic markers were evident after respiration is removed. Specifically, the LF/HF ratio slightly decreased on average from pre to post reconstruction for controls, increased on average for COPD. In healthy controls, respiration frequency is distributed across multiple bandwidths, causing large decreases in both LF and HF when removed. With respiration effect removed from COPD population, LE dominates autonomic response, indicating that the frequency is concentrated in the HF autonomic region. Decrease in variance of data set increases probability tat smaller changes can be detected in values. The theory set forth in hypothesis two was validated by the quantification of a correlation between peripheral and central autonomic influences, as evidenced by differences in oculomotor adaptability correlating with differences in HRV. Standard Deviation varies with grouping, not with age. Increasing controlled respiration frequencies resulted in adaptive presbyopes and controls displaying similar sympathetic responses, diverging from non-adaptive group. WayS reduced frequency content in ranges concurrent with breathing rate, indicating a robust analysis. The outcome of hypothesis three was the confirmation that wavelet statistical methods possess significant potential for applications in HRV. Entropy can be used in conjunction with cluster analysis to classify patient populations with high accuracy. Using the WayS analysis, the respiration effect can be removed from HRV data sets, providing new insights into autonomic alterations, both central and peripheral, in disease