Hazar Ashouri

Weighing Scale-Based Pulse Transit Time is a Superior Marker of Blood Pressure than Conventional Pulse Arrival Time

Pulse transit time (PTT) is being widely pursued for cuff-less blood pressure (BP) monitoring. Most efforts have employed the time delay between ECG and finger photoplethysmography (PPG) waveforms as a convenient surrogate of PTT. However, these conventional pulse arrival time (PAT) measurements include the pre-ejection period (PEP) and the time delay through small, muscular arteries and may thus be an unreliable marker of BP. We assessed a bathroom weighing scale-like system for convenient measurement of ballistocardiography and foot PPG waveforms – and thus PTT through larger, more elastic arteries – in terms of its ability to improve tracking of BP in individual subjects. We measured “scale PTT”, conventional PAT, and cuff BP in humans during interventions that increased BP but changed PEP and smooth muscle contraction differently. Scale PTT tracked the diastolic BP changes well, with correlation coefficient of −0.80 ± 0.02 (mean ± SE) and root-mean-squared-error of 7.6 ± 0.5 mmHg after a best-case calibration. Conventional PAT was significantly inferior in tracking these changes, with correlation coefficient of −0.60 ± 0.04 and root-mean-squared-error of 14.6 ± 1.5 mmHg (p < 0.05). Scale PTT also tracked the systolic BP changes better than conventional PAT but not to an acceptable level. With further development, scale PTT may permit reliable, convenient measurement of BP.

Quantifying and Reducing Motion Artifacts in Wearable Seismocardiogram Measurements During Walking to Assess Left Ventricular Health

Goal: Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subjects preferred pace) and moderately fast (1.34–1.45 m/s) speeds. Methods: We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking. Results: The EMD-based denoising approach provides a statistically significant increase in the signal-to-noise ratio of wearable SCG signals and also improves estimation of PEP during walking. Conclusion: The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking. Significance: A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high-quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement—aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular systems response to stress (exercise), and thus provide a more holistic assessment of overall health.

Unobtrusive Estimation of Cardiac Contractility and Stroke Volume Changes Using Ballistocardiogram Measurements on a High Bandwidth Force Plate

Unobtrusive and inexpensive technologies for monitoring the cardiovascular health of heart failure (HF) patients outside the clinic can potentially improve their continuity of care by enabling therapies to be adjusted dynamically based on the changing needs of the patients. Specifically, cardiac contractility and stroke volume (SV) are two key aspects of cardiovascular health that change significantly for HF patients as their condition worsens, yet these parameters are typically measured only in hospital/clinical settings, or with implantable sensors. In this work, we demonstrate accurate measurement of cardiac contractility (based on pre-ejection period, PEP, timings) and SV changes in subjects using ballistocardiogram (BCG) signals detected via a high bandwidth force plate. The measurement is unobtrusive, as it simply requires the subject to stand still on the force plate while holding electrodes in the hands for simultaneous electrocardiogram (ECG) detection. Specifically, we aimed to assess whether the high bandwidth force plate can provide accuracy beyond what is achieved using modified weighing scales we have developed in prior studies, based on timing intervals, as well as signal-to-noise ratio (SNR) estimates. Our results indicate that the force plate BCG measurement provides more accurate timing information and allows for better estimation of PEP than the scale BCG (r2 = 0.85 vs. r2 = 0.81) during resting conditions. This correlation is stronger during recovery after exercise due to more significant changes in PEP (r2 = 0.92). The improvement in accuracy can be attributed to the wider bandwidth of the force plate. ∆SV (i.e., changes in stroke volume) estimations from the force plate BCG resulted in an average error percentage of 5.3% with a standard deviation of ±4.2% across all subjects. Finally, SNR calculations showed slightly better SNR in the force plate measurements among all subjects but the small difference confirmed that SNR is limited by motion artifacts rather than instrumentation.

Elucidating the Hemodynamic Origin of Ballistocardiographic Forces: Toward Improved Monitoring of Cardiovascular Health at Home

The ballistocardiogram (BCG), a signal describing the reaction forces of the body to cardiac ejection of blood, has recently gained interest in the research community as a potential tool for monitoring the mechanical aspects of cardiovascular health for patients at home and during normal activities of daily living. An important limitation in the field of BCG research is that while the BCG signal measures the forces of the body, the information desired (and understood) by clinicians and caregivers, regarding mechanical health of the cardiovascular system, is typically expressed as blood pressure or flow. This paper aims to explore, using system identification tools, the mathematical relationship between the BCG signal and the better-understood impedance cardiography (ICG) and arterial blood pressure (ABP) waveforms, with a series of human subject studies designed to asynchronously modulate cardiac output and blood pressure and with different magnitudes. With this approach, we demonstrate for 19 healthy subjects that the BCG waveform more closely maps to the ICG (flow) waveform as compared with the finger-cuff-based ABP (pressure) waveform, and that the BCG can provide a more accurate estimate of stroke volume (r=0.73, p <; 0.05) as compared with pulse pressure changes (r = 0.26). We also examined, as a feasibility study, for one subject, the ability to calibrate the BCG measurement tool with an ICG measurement on the first day, and then track changes in stroke volume on subsequent days. Accordingly, we conclude that the BCG is a signal more closely related to blood flow than pressures, and that a key health parameter for titrating care-stroke volume-can potentially be accurately measured with BCG signals at home using unobtrusive and inexpensive hardware, such as a modified weighing scale, as compared with the state-of-the-art ICG and ABP devices, which are expensive and obtrusive for use at home.

Improving the accuracy of proximal timing detection from ballistocardiogram signals using a high bandwidth force plate

Ballistocardiography (BCG) is a non-invasive measure of the reactionary forces of the body to cardiac ejection of blood into the vasculature. BCG signals are currently measured by modified weighing scales, chairs, beds, or wearable tri-axial accelerometers. In this work, we measure the BCG using a high bandwidth multicomponent force plate and compare the timing accuracy of our measured signals with a weighing scale BCG that we have developed and verified in prior studies. We also examine the signal-to-noise ratio (SNR) of BCG signals obtained with each of the two instruments. Our results indicate that the force plate BCG measurement provides more accurate timing information as it allows for more reliable prediction of the pre-ejection period (PEP) - the time the ventricles spend in isovolumetric contraction - than the scale BCG (r2=0.83 vs r2=0.73). The SNR comparison results were as follows: although the force plate BCG has a slightly better SNR, the improvement is not significant and we conclude that SNR is limited by motion artifacts rather than instrumentation.

Automatic Detection of Seismocardiogram Sensor Misplacement for Robust Pre-Ejection Period Estimation in Unsupervised Settings

Seismocardiography (SCG), the measurement of the local chest vibrations due to the movements of blood and the heart, is a non-invasive technique for assessing myocardial contractility via the pre-ejection period (PEP). Recently, SCG-based extraction of PEP has been shown to be an effective means of classifying decompensated from compensated heart failure patients, and thus can be potentially used for monitoring such patients at home. Accurate extraction of PEP from SCG signals hinges on laboratory-based population data (i.e., regression curves) linking particular time-domain features of the SCG signal to corresponding features from reference standard bulky instruments such as impedance cardiography (ICG). Such regression curves, in the case of SCG, have always been estimated based on the “ideal” positioning of the SCG sensor on the chest. However, in settings, such as the home, where users may position the SCG measurement hardware on the chest without supervision, it is likely that the sensor will not always be placed exactly on this “ideal” location on the sternum, but rather on other positions on the chest as well. In this paper, we show for the first time that the regression curve for estimating PEP from SCG signals differs significantly as the position of the sensor changes. We further devise a method to automatically detect when the sensor is placed in any position other than the desired one in order to avoid inaccurate systolic time interval estimation. Our classification algorithm for this purpose resulted in 0.83 precision and 0.82 recall when classifying whether the sensor is placed in the desired position or not. The classifier was tested with heartbeats taken both at rest, and also during exercise recovery to ensure that waveform changes due to positioning could be accurately discriminated from those due to physiological effects.

Universal Pre-Ejection Period Estimation using Seismocardiography: Quantifying the Effects of Sensor Placement and Regression Algorithms

Seismocardiography (SCG), the measurement of local chest vibrations due to the heart and blood movement, is a noninvasive technique to assess cardiac contractility via systolic time intervals such as the pre-ejection period (PEP). Recent studies show that SCG signals measured before and after exercise can effectively classify compensated and decompensated heart failure patients through PEP estimation. However, the morphology of the SCG signal varies from person to person and sensor placement making it difficult to automatically estimate PEP from SCG and electrocardiogram signals using a global model. In this proof-of-concept study, we address this problem by extracting a set of timing features from SCG signals measured from multiple positions on the upper body. We then test global regression models that combine all the detected features to identify the most accurate model for PEP estimation obtained from the best performing regressor and the best sensor location or combination of locations. Our results show that ensemble regression using extreme gradient boosting with a combination of sensors placed on the sternum and below the left clavicle provide the best RMSE = 11.6 ± 0.4 ms across all subjects. We also show that placing the sensor below the left or right clavicle rather than the conventional placement on the sternum results in more accurate PEP estimates.

Towards ubiquitous blood pressure monitoring in an armband using pulse transit time

Unobtrusive, continuous, and convenient blood pressure (BP) measurement technologies are of high importance to control hypertension and allow titration of care. In this paper, we present novel technologies to facilitate development of an armband system to estimate BP using pulse transit time (PTT) obtained from Ballistocardiogram (BCG) and Photo-plethysmogram (PPG) signals. Specifically, a wearable, acceleration based BCG measurement positioned on the upper arm at the tricep (for proximal timing detection) is paired with a reflectance mode PPG measurement on the inner side of the same arm (for distal timing detection) to extract an estimate of PTT. This unconventional PTT estimate, derived from a relatively short segment of the arterial tree (from the aortic arch to the microvascular bed at the upper arm), was compared to a more established PTT measurement (from the aortic arch to the toe) in four subjects in a pilot study to evaluate feasibility. The PTT measurements were reasonably well correlated for all subjects (r2 = 0.83) for the perturbations employed in this work (hand grip challenge and slow breathing). The results suggest that future investigation of an armband-deployed BCG-PPG based system for PTT measurement is warranted.

Author Correction: Weighing Scale-Based Pulse Transit Time is a Superior Marker of Blood Pressure than Conventional Pulse Arrival Time

A correction has been published and is appended to both the HTML and PDF versions of this paper. The error has not been fixed in the paper.