Toward Continuous, Noninvasive Assessment of Ventricular Function and Hemodynamics: Wearable Ballistocardiography

Ballistocardiography, the measurement of the reaction forces of the body to cardiac ejection of blood, is one of the few techniques available for unobtrusively assessing the mechanical aspects of cardiovascular health outside clinical settings. Recently, multiple experimental studies involving healthy subjects and subjects with various cardiovascular diseases have demonstrated that the ballistocardiogram (BCG) signal can be used to trend cardiac output, contractility, and beat-by-beat ventricular function for arrhythmias. The majority of these studies has been performed with "fixed" BCG instrumentation-such as weighing scales or chairs-rather than wearable measurements. Enabling wearable, and thus continuous, recording of BCG signals would greatly expand the capabilities of the technique; however, BCG signals measured using wearable devices are morphologically dissimilar to measurements from "fixed" instruments, precluding the analysis and interpretation techniques from one domain to be applied to the other. In particular, the time intervals between the electrocardiogram (ECG) and BCG-namely, the R-J interval, a surrogate for measuring contractility changes-are significantly different for the accelerometer compared to a "fixed" BCG measurement. This paper addresses this need for quantitatively normalizing wearable BCG measurement to "fixed" measurements with a systematic experimental approach. With these methods, the same analysis and interpretation techniques developed over the past decade for "fixed" BCG measurement can be successfully translated to wearable measurements

Extracting Cardiac Information From Medical Radar Using Locally Projective Adaptive Signal Separation

Electrocardiography is the gold standard for electrical heartbeat activity, but offers no direct measurement of mechanical activity. Mechanical cardiac activity can be assessed non-invasively using, e.g., ballistocardiography and recently, medical radar has emerged as a contactless alternative modality. However, all modalities for measuring the mechanical cardiac activity are affected by respiratory movements, requiring a signal separation step before higher-level analysis can be performed. This paper adapts a non-linear filter for separating the respiratory and cardiac signal components of radar recordings. In addition, we present an adaptive algorithm for estimating the parameters for the non-linear filter. The novelty of our method lies in the combination of the non-linear signal separation method with a novel, adaptive parameter estimation method specifically designed for the non-linear signal separation method, eliminating the need for manual intervention and resulting in a fully adaptive algorithm. Using the two benchmark applications of (i) cardiac template extraction from radar and (ii) peak timing analysis, we demonstrate that the non-linear filter combined with adaptive parameter estimation delivers superior results compared to linear filtering. The results show that using locally projective adaptive signal separation (LoPASS), we are able to reduce the mean standard deviation of the cardiac template by at least a factor of 2 across all subjects. In addition, using LoPASS, 9 out of 10 subjects show significant (at a confidence level of 2.5%) correlation between the R-T-interval and the R-radar-interval, while using linear filters this ratio drops to 6 out of 10. Our analysis suggests that the improvement is due to better preservation of the cardiac signal morphology by the non-linear signal separation method. Hence, we expect that the non-linear signal separation method introduced in this paper will mostly benefit analysis methods investigating the cardiac radar signal morphology on a beat-to-beat basis

Vital Sign Monitoring in Automotive Environments

Diplomová práce je věnována problematice monitorování vitálních funkcí v automobilním prostředí. Teoretická část je popsána formou ucelené rešerše, která shrnuje aktuálně dostupné monitorovací metody pro neinvazivní snímání životně důležitých parametrů v automobilním zařízení. Experimentální část práce popisuje návrh a realizaci pneumatického systému, který bude následně integrován do autosedačky nebo bezpečnostního pásu. Součástí praktické části je také návrh a realizace experimentálních měření pro stanovení přesnosti navržených senzorů v reálném prostředí automobilu. Obsah práce je doplněn o testování vlivu typu materiálu, tvaru, velikosti, zapouzdření a umístění senzoru, způsobu zpracování naměřených signálů a různých podmínek jízdy v automobilu. Závěr práce patří statistickému vyhodnocení dosaženích výsledků. Hodnocení využívá srovnání zpracované průměrné variability srdečného tepu extrahovaného z balistokardiografického signálu vůči referenci (elektrokardiografický signál). Kompletní program včetně zpracování dat je zpracován v programovém prostředí Matlab.The scope of this thesis is vital sign monitoring in automotive enviroments. The theoretical part is written in a form of a comprehensive research, which summarizes the currently available monitoring methods for non-invasive sensing of vital parameters in automobiles. The aim of the experimental part is to design and implement a pneumatic system that will be integrated into a car seat or seat belt. Experimental part also includes the design and implementation of experimental measurements that determine the accuracy of the designed sensors in a real car application. The content of this thesis is complemented by testing the impact of the type of material, shape, size, encapsulation and location of the sensors, the type of the processing method and various driving conditions in the car. The conclusion of the thesis is dedicated to the statistical evaluation of the results. The comparison of the reference with the processed average heart rate variability extracted from the ballistocardiography (electrocardiography) is used for the statistical evaluation. The complete program including the data processing is written in Matlab.450 - Katedra kybernetiky a biomedicínského inženýrstvídobř

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

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference