Using ballistocardiography to measure cardiac performance: a brief review of its history and future significance

Vogt, Emelie; MacQuarrie, David S.; Neary, J. Patrick

doi:10.1111/j.1475-097x.2012.01150.x

Cited by 34 publications

(18 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…5 These include ballistocardiography, SCG, apexcardiography, radar SCG, and others. 5 Ballistocardiography signals represent movements of the whole body in response to cardiac ejection of blood into the vasculature, 5,12,13 and SCG corresponds to local vibrations of the chest wall associated with the heartbeat, blood flow, respiration, body movements, and so on. [5][6][7] SCG is recorded from the surface of the body using accelerometers, and contains waves corresponding to atrial and ventricular contraction, LV filling, closing and opening of the atrioventricular and semilunar valves, and maximal acceleration in the aorta.…”

Section: Discussionmentioning

confidence: 99%

Noninvasive Monitoring of Cardiac Preload and Contractility by Heart Beat-Derived Mechanical Vibration on the Chest Wall in Guinea Pigs

Adachi¹,

Ohba²,

Fujisawa³

et al. 2016

J.ATAMIS

View full text Add to dashboard Cite

Background: Myocardial motion produces compression waves that are transmitted to the surface of the chest and vibrate the chest wall. Piezoelectric transducers, which detect mechanical vibrations, may be useful for evaluating cardiac and/or breathing movements when the sensor is placed under an animal’s body.Methods: We assessed heartbeat-related chest vibration signals (CVS) detected with a piezoelectric transducer in anesthetized guinea pigs while simultaneously monitoring the electrocardiogram, heart sounds, aortic pressure, and central venous pressure.Results: CVS displayed characteristic features as the ventricle and/or atrium contracted and relaxed during cardiac cycles. A transient positive wave, which occurred approximately at the onset of the R wave on the electrocardiogram, was followed by a profound negative wave during systole. The negative wave peaked early in systole and then gradually returned. A small notch was observed at the second heart sound. A dome-shaped positive wave was observed during diastole that connected to the transient positive wave of the next cardiac cycle. In response to phlebotomy (blood volume, 15 ml/kg), the size of the dome-shaped positive wave decreased, whereas saline transfusion (20 ml/kg) increased the positive wave during diastole. Administration of isoproterenol markedly increased the transient positive wave at the onset of systole, whereas phenylephrine affected the transient positive wave only slightly.Conclusions: Our findings suggest that CVS may be useful to identify cardiac cycles and to evaluate cardiovascular dynamics during changes in blood volume and/or cardiac contractility.

show abstract

Section: Discussionmentioning

confidence: 99%

Noninvasive Monitoring of Cardiac Preload and Contractility by Heart Beat-Derived Mechanical Vibration on the Chest Wall in Guinea Pigs

Adachi¹,

Ohba²,

Fujisawa³

et al. 2016

J.ATAMIS

View full text Add to dashboard Cite

show abstract

“…Ballistocardiography has been used to detect the displacement of the body from the ejection of blood and contraction of the heart and thus detects inertial cardiohemic oscillations (Giovangrandi et al, 2011;Vogt et al, 2012). The late-systolic net inflow caused by inertia may therefore be a part of the oscillations detected by ballistocardiography, specifically the K-wave.…”

Section: Figurementioning

confidence: 99%

Heart filling exceeds emptying during late ventricular systole in patients with systolic heart failure and healthy subjects – a cardiac MRI study

Carlsson

Kanski

Borgquist

et al. 2018

Clin Physio Funct Imaging

View full text Add to dashboard Cite

Summary Background Total heart volume (THV) within the pericardium is not constant throughout the cardiac cycle and THV would intuitively be lowest at end systole. We have, however, observed a phase shift between ventricular outflow and atrial inflow which causes the minimum THV to occur before end systole. The aims were to explain the mechanism of the late‐systolic net inflow to the heart and determine whether this net inflow is affected by increased cardiac output or systolic heart failure. Methods and Results Healthy controls (n = 21) and patients with EF<35% (n = 14) underwent magnetic resonance imaging with flow measurements in vessels to and from the heart, and this was repeated in nine controls during 140 μgram kg−1 min−1 adenosine infusion. Minimum THV occurred 78 ± 6 ms before end of systolic ejection (8 ± 1% of the cardiac cycle) in controls. The late‐systolic net inflow was 12·3 ± 1·1 ml or 6·0 ± 0·5% of total stroke volume (TSV). Cardiac output increased 66 ± 8% during adenosine but late‐systolic net inflow to the heart did not change (P = 0·73). In patients with heart failure, late‐systolic net inflow of the heart′s left side was lower (3·4 ± 0·5%) compared to healthy subjects (5·3 ± 0·6%, P = 0·03). Conclusions Heart size increases before end systole due to a late‐systolic net inflow which is unaffected by increased cardiac output. This may be explained by inertia of blood that flows into the atria generated by ventricular systole. The lower late‐systolic net inflow in patients with systolic heart failure may be a measure of decreased ventricular filling due to decreased systolic function, thus linking systolic to diastolic dysfunction.

show abstract

“…This shift comprises several components as a result of cardiac activity, respiration, and body movements. This shifting of the center of mass of the body generates the BCG waveform since the blood distribution changes during the cardiac cycle [5]. More than 100 years ago, BCG failed to prove its functionality, and it did not start to be used in routine tasks for a few general reasons as follows.…”

Section: Introductionmentioning

confidence: 99%

Ballistocardiogram signal processing: a review

Sadek

Biswas

Abdulrazak

2019

Health Inf Sci Syst

139

View full text Add to dashboard Cite

There are several algorithms for analyzing and interpreting cardiorespiratory signals obtained from in-bed based sensors. In sum, these algorithms can be broadly grouped into three categories: time-domain algorithms, frequency-domain algorithms, and wavelet-domain algorithms. A summary of these algorithms is given below to highlight which category of algorithms will be used in our analysis. First, time-domain algorithms are mainly focused on detecting local maxima or local minima using a moving window, and therefore finding the interval between the dominant J-peaks of ballistocardiogram signal. However, this approach has many limitations because of the nonlinear and nonstationary behavior of the ballistocardiogram signal. The implication is that the ballistocardiogram signal does not display consistent J-peaks, which can usually be the case for overnight, in-home monitoring, particularly with frail elderly. Additionally, its accuracy will be undoubtedly affected by motion artifacts. Second, frequency-domain algorithms do not provide information about interbeat intervals. Nevertheless, they can provide information about heart rate variability. This is usually done by taking the fast Fourier transform or the inverse Fourier transform of the logarithm of the estimated spectrum, i.e., cepstrum of the signal using a sliding window. Thereafter, the dominant frequency is obtained in a particular frequency range. The limit of these algorithms is that the peak in the spectrum may get wider and multiple peaks may appear, which might cause a problem in measuring the vital signs. At last, the objective of wavelet-domain algorithms is to decompose the signal into different components, hence the component which shows an agreement with the vital signs can be selected. In other words, the selected component contains only information about the heart cycles or respiratory cycles, respectively. Interbeat intervals can be found easily by applying a simple peak detector. An empirical mode decomposition is an alternative approach to wavelet decomposition, and it is also a very suitable approach to cope with nonlinear and nonstationary signals such as cardiorespiratory signals. Apart from the above-mentioned algorithms, machine learning approaches have been implemented for measuring heartbeats. However, manual labeling of training data is a restricting property. Furthermore, the training step should be repeated whenever the data collection protocol has been changed.

show abstract

Using ballistocardiography to measure cardiac performance: a brief review of its history and future significance

Cited by 34 publications

References 26 publications

Noninvasive Monitoring of Cardiac Preload and Contractility by Heart Beat-Derived Mechanical Vibration on the Chest Wall in Guinea Pigs

Noninvasive Monitoring of Cardiac Preload and Contractility by Heart Beat-Derived Mechanical Vibration on the Chest Wall in Guinea Pigs

Heart filling exceeds emptying during late ventricular systole in patients with systolic heart failure and healthy subjects – a cardiac MRI study

Ballistocardiogram signal processing: a review

Contact Info

Product

Resources

About