Ballistocardiograms Research Articles

Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging

Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.

Non-Invasive Heart Rate Estimation From Ballistocardiograms Using Bidirectional LSTM Regression.

Non-invasive heart rate estimation is of great importance in daily monitoring of cardiovascular diseases. In this paper, a bidirectional long short term memory (bi-LSTM) regression network is developed for non-invasive heart rate estimation from the ballistocardiograms (BCG) signals. The proposed deep regression model provides an effective solution to the existing challenges in BCG heart rate estimation, such as the mismatch between the BCG signals and ground-truth reference, multi-sensor fusion and effective time series feature learning. Allowing label uncertainty in the estimation can reduce the manual cost of data annotation while further improving the heart rate estimation performance. Compared with the state-of-the-art BCG heart rate estimation methods, the strong fitting and generalization ability of the proposed deep regression model maintains better robustness to noise (e.g., sensor noise) and perturbations (e.g., body movements) in the BCG signals and provides a more reliable solution for long term heart rate monitoring.

Bed-Based Ballistocardiography: Dataset and Ability to Track Cardiovascular Parameters.

Background: The goal of this work was to create a sharable dataset of heart-driven signals, including ballistocardiograms (BCGs) and time-aligned electrocardiograms (ECGs), photoplethysmograms (PPGs), and blood pressure waveforms. Methods: A custom, bed-based ballistocardiographic system is described in detail. Affiliated cardiopulmonary signals are acquired using a GE Datex CardioCap 5 patient monitor (which collects ECG and PPG data) and a Finapres Medical Systems Finometer PRO (which provides continuous reconstructed brachial artery pressure waveforms and derived cardiovascular parameters). Results: Data were collected from 40 participants, 4 of whom had been or were currently diagnosed with a heart condition at the time they enrolled in the study. An investigation revealed that features extracted from a BCG could be used to track changes in systolic blood pressure (Pearson correlation coefficient of 0.54 +/− 0.15), dP/dtmax (Pearson correlation coefficient of 0.51 +/− 0.18), and stroke volume (Pearson correlation coefficient of 0.54 +/− 0.17). Conclusion: A collection of synchronized, heart-driven signals, including BCGs, ECGs, PPGs, and blood pressure waveforms, was acquired and made publicly available. An initial study indicated that bed-based ballistocardiography can be used to track beat-to-beat changes in systolic blood pressure and stroke volume. Significance: To the best of the authors’ knowledge, no other database that includes time-aligned ECG, PPG, BCG, and continuous blood pressure data is available to the public. This dataset could be used by other researchers for algorithm testing and development in this fast-growing field of health assessment, without requiring these individuals to invest considerable time and resources into hardware development and data collection.

An Effective Algorithm for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms

Ballistocardiograms (BCG) is an essential signal for vital sign monitoring. Obtaining the beat-to-beat intervals from BCG signal is of great significance for home-care applications, such as sleep staging, heart disease alerting, etc. The current approaches of detecting beat-to-beat intervals from BCG signals are complex. In this paper, we develop a non-invasive BCG monitoring system, and propose an effective and accurate algorithm for beat-to-beat detection. Firstly, a heartbeat shape is adaptively modeled based on a two-step procedure by taking advantage of the J-peak and the K-valley of BCG signals. Then, forward and backward detections with the criteria of both the morphological distance and the cross-correlation are jointly employed to find the position of each BCG signal, and in turn, to determine the beat-to-beat intervals of BCG signals. The proposed method was validated in at least 90 minutes recording from 10 subjects in various setups. The mean absolute beat-to-beat intervals error was 10.72 ms and on an average 97.93% of the beat-to-beat intervals were detected.

Robust inter-beat interval estimation in cardiac vibration signals

Reliable and accurate estimation of instantaneous frequencies of physiological rhythms, such as heart rate, is critical for many healthcare applications. Robust estimation is especially challenging when novel unobtrusive sensors are used for continuous health monitoring in uncontrolled environments, because these sensors can create significant amounts of potentially unreliable data. We propose a new flexible algorithm for the robust estimation of local (beat-to-beat) intervals from cardiac vibration signals, specifically ballistocardiograms (BCGs), recorded by an unobtrusive bed-mounted sensor. This sensor allows the measurement of motions of the body which are caused by cardiac activity. Our method requires neither a training phase nor any prior knowledge about the morphology of the heart beats in the analyzed waveforms. Instead, three short-time estimators are combined using a Bayesian approach to continuously estimate the inter-beat intervals. We have validated our method on over-night BCG recordings from 33 subjects (8 normal, 25 insomniacs). On this dataset, containing approximately one million heart beats, our method achieved a mean beat-to-beat interval error of 0.78% with a coverage of 72.69%.

Ballistocardiography in the Physics Classroom

Ballistocardiograms (BCGs) are graphical representations of the recoil from the pumping action of the heart. Introductory mechanics and a basic knowledge of the circulatory system are sufficient to understand the key features of a BCG. BCGs with adequate detail for instructional purposes may be produced easily and rapidly using hardware (a force plate) and software common to introductory physics labs. The interdisciplinary nature of this exercise is a particularly attractive feature. We have used it as a demonstration in a course on mechanics for nonphysics majors as well as for visiting high school students, and indicate here possible ways to incorporate it into upper-level physics or biomechanics courses.

Left ventricular dysfunction discriminated noninvasively beat by beat

To discriminate left ventricular (LV) dysfunctional beats noninvasively, simultaneous systolic time intervals (STI), acceleration ballistocardiograms (BCG) and respiratory cycle were recorded in 102 men with no history of cardiac disease and in 20 men with proved myocardial infarction or systemic hypertension. Thirty-six measurements were made on each heartbeat on a maximum of 20 beats per patient. The ratio of preejection period to LV ejection time (ET) varied significantly with the respiratory cycle in normal men, but not in men with proved myocardial infarctions or systemic hypertension. The BCG l-wave amplitude varied significantly with respiration in all 3 groups. Various statistical methods identified 9 primary variables of the 36, which differed significantly in a training group of 275 heartbeats in 19 normal vs 46 beats in 5 coronary patients. A nonlinear quadratic discriminant function based on these primary variables identified 87% of the heartbeats as “normal” in the normal men and 98% as “coronary” in the coronary men. A test group of normal men older than 40 years showed 89% of 97 heartbeats “normal” and in hypertensive men 67% of 85 beats “normal.” Comparison by patients showed a range of “coronary” beats in the training normal group from 0 to 50%, the older normal group 0 to 33% and in the hypertensive patients 0 to 88 %. The appropriate use of quadratic discriminant analysis to multivariable data may yield information not otherwise seen. A beat-by-beat identification of dysfunctional beats at rest may lead to a sensitive index that is prognostically useful.

A Study on the Ballistocardiogram m Auricular Fibrillation by Means of Harmonic Analysis

The ballistocardiograms (BCGs) of the cases with auricular fibrillation are generally complicated in their waves. Therefore, it is not easy to analyse the BCGs of such patients by routine method. Harmonic analysis was applied in order to interprete such BCG accurately. The examines consisted of 15 cases of auricular fibrillation with mitral valvular disease and 30 cases of auricular fibrillation with hypertensive heart diseases and 10 healthy persons, totaling 55 cases. The longitudinal BCGs were recorded during rest breathing for one minute on a magnetic tape by using von Wittern-type sublow-frequency table and a Sony data recorder (PWA-23, MA-33). The recorded magnetic tape was rerecorded on a magnetic tape of a Sony correlater (CCA-32 type). Autocorrelograms of BCGs were obtained by setting the correlater with the rerecorded magnetic tape. All the autocorrelograms of the patients of auricular fibrillation showed damping patterns, and they were classified into three types. Type 1 consists mainly of aperiodic component with a little random component. Type 2 consists of aperiodic component and of random component. Type 3 consists only of random component. Among the autocorrelograms of BCGs of 45 cases with auricular fibrillation, 29 cases (44%) belonged to Type 1, 15 cases (33%) to Type 2, and 10 cases (22%) to type 3. The power density spectra of BCGs were obtained by the Fourier transformation of the autocorrelation function. The power density spectrum of BCG in the auricular fibrillation. either with mitral valvular diseases or with hypertensive heart diseases, damped slowly, and then converged on the zero line about 10 cps. But it showed an abnormal fluctuation over the frequency zone from 3 cps to 6 cps in all the cases of the auricular fibrillation with mitral valvular diseases. The curves of the power density spectra of BCGs showed sinusoidal wave patterns of small amplitude in all the cases of auricular fibrillation with hypertensive heart diseases. This pattern differed distinctly from that of auricular fibrillation with mitral valvular diseases. In healthy persons, the curves of the power density spectra of BCGs showed regular and damping sinusoidal wave patterns.