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 of 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 have 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.
Ballistocardiography is a non-invasive measurement of the mechanical movement of the body caused by cardiac ejection of blood. Recent studies have demonstrated that ballistocardiogram (BCG) signals can be measured using a modified home weighing scale, and used to track changes in myocardial contractility and cardiac output. With this approach, the BCG can potentially be used both for preventive screening and for chronic disease management applications. However, for achieving high signal quality, subjects are required to stand still on the scale in an upright position for the measurement; the effects of intentional (for user comfort) or unintentional (due to user error) modifications in the position or posture of the subject during the measurement have not been investigated in the existing literature. In this study, we quantified the effects of different standing and seated postures on the measured BCG signals, and on the most salient BCG-derived features compared to reference standard measurements (e.g., impedance cardiography). We determined that the standing upright posture led to the least distorted signals as hypothesized, and that the correlation between BCG-derived timing interval features (R-J interval) and the pre-ejection period, PEP (measured using ICG), decreased significantly with impaired posture or sitting position. We further implemented two novel approaches to improve the PEP estimates from other standing and sitting postures, using system identification and improved J-wave detection methods. These approaches can improve the usability of standing BCG measurements in unsupervised settings (i.e. the home), by improving the robustness to non-ideal posture, as well as enabling high quality seated BCG measurements.
Recent advances have led to renewed interest in ballistocardiography (BCG), a non-invasive measure of the small reaction forces on the body from cardiovascular events. A broad range of platforms have been developed and verified for BCG measurement including beds, chairs, and weighing scales: while the body is coupled to such a platform, the cardiogenic movements of the center-of-mass (COM) are measured. Wearable BCG, measured with an accelerometer affixed to the body, may enable continuous, or more regular, monitoring during the day; however, the signals from such wearable BCGs represent local or distal accelerations of skin and tissue rather than the displacement of the body's COM. In this paper we propose a novel method to reconstruct the COM BCG from a wearable sensor via a training step to remove these local effects. Preliminary validation of this method was performed with fifteen subjects: the wearable sensor was placed at three locations on the surface of the body while COM BCG measurements were recorded simultaneously with a modified weighing scale. A regularized system identification approach was used to reconstruct the COM BCG from the wearable signal. Preliminary results suggest that the relationship between local and central forces is highly dependent on both the individual and the location where the wearable sensor is placed on the body and that these differences can be resolved via calibration to accurately measure changes in cardiac output and contractility from a wearable sensor. Such measurements could be highly effective, for example, for improved monitoring of heart failure patients at home.
The recent resurgence of ballistocardiogram (BCG) measurement and interpretation technologies has led to a wide range of powerful tools available for unobtrusively assessing mechanical aspects of cardiovascular health at home. Researchers have demonstrated a multitude of modern BCG measurement modalities, including beds, chairs, weighing scales, and wearable approaches. However, many modalities produce significant variations in the morphology of the measured BCG, creating confusion in the analysis and interpretation of the signals. This paper creates a framework for comparing wearable BCG measurements to whole body measurements—such as taken with a weighing scale system—to eventually allow the same analysis and interpretation tools that have been developed for whole body systems to be applied in the future to wearable systems. To the best of our knowledge, it represents the first attempt to morphologically compare vertical acceleration recordings measured on different locations on the torso to whole body displacements measured by BCG instrumentation.
In this paper we describe a new method to measure aortic valve opening (AVO) and closing (AVC) from cardiogenic limb vibrations (i.e., wearable ballistocardiogram [BCG] signals). AVO and AVC were detected for each heartbeat with accelerometers on the upper arm (A), wrist (W), and knee (K) of 22 subjects following isometric exercise. Exercise-induced changes were recorded with impedance cardiography. The method, Filter BCG, detects peaks in distal vibrations after filtering with individually-tuned bandpass filters. In agreement with recent studies, we did not find peaks at AVO and AVC in limb vibrations directly. Interestingly, distal vibrations filtered with FilterBCG yielded reliable peaks at AVO (r2 = 0.95 A, 0.94 W, 0.77 K) and AVC (r2= 0.92 A, 0.89 W, 0.68 K). FilterBCG measures AVO and AVC accurately from arm, wrist, and knee vibrations, and it outperforms the standard R-J interval method.
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