Ballistocardiogram Recordings Research Articles

Non-Contact Heartbeat Detection Based on Ballistocardiogram Using UNet and Bidirectional Long Short-Term Memory.

Benefiting from non-invasive sensing tech- nologies, heartbeat detection from ballistocardiogram (BCG) signals is of great significance for home-care applications, such as risk prediction of cardiovascular disease (CVD) and sleep staging, etc. In this paper, we propose an effective deep learning model for automatic heartbeat detection from BCG signals based on UNet and bidirectional long short-term memory (Bi-LSTM). The developed deep learning model provides an effective solution to the existing challenges in BCG-aided heartbeat detection, especially for BCG in low signal-to-noise ratio, in which the waveforms in BCG signals are irregular due to measured postures, rhythm and artifact motion. For validations, performance of the proposed detection is evaluated by BCG recordings from 43 subjects with different measured postures and heart rate ranges. The accuracy of the detected heartbeat intervals measured in different postures and signal qualities, in comparison with the R-R interval of ECG, is promising in terms of mean absolute error and mean relative error, respectively, which is superior to the state-of-the-art methods. Numerical results demonstrate that the proposed UNet-BiLSTM model performs robust to noise and perturbations (e.g. respiratory effort and artifact motion) in BCG signals, and provides a reliable solution to long term heart rate monitoring.

An Unobtrusive System for Heart Rate Monitoring Based on Ballistocardiogram Using Hilbert Transform and Viterbi Decoding

This paper presents an unobtrusive system for monitoring heart rate (HR) based on ballistocardiogram (BCG). The system contains a piezoelectric sensor, which can be embedded into a chair or bed, to convert small body vibrations caused by heart beating into BCG signal and a novel algorithm to estimate HR from the BCG. The algorithm employs the Hilbert Transform to reveal the frequency content of J-peak in the BCG signal for HR estimation. The Viterbi Decoding is applied to improve the HR accuracy for noisy BCG signal by finding the most likely path through time-frequency state-space plane. The performance of the proposed algorithm is evaluated by BCG recordings from 32 subjects. Mean absolute error (MAE) of 2.17 beats per minute (BPM) and standard deviation of absolute error (SDAE) of 2.34 BPM are achieved. Pearson correlation coefficient of 0.956 between estimated HR and true HR is obtained.

Classification of Decompensated Heart Failure From Clinical and Home Ballistocardiography.

To improve home monitoring of heart failure patients so as to reduce emergency room visits and hospital readmissions. We aim to do this by analyzing the ballistocardiogram (BCG) to evaluate the clinical state of the patient. 1) High quality BCG signals were collected at home from HF patients after discharge. 2) The BCG recordings were preprocessed to exclude outliers and artifacts. 3) Parameters of the BCG that contain information about the cardiovascular system were extracted. These features were used for the task of classification of the BCG recording based on the status of HF. The best AUC score for the task of classification obtained was 0.78 using slight variant of the leave one subject out validation method. This work demonstrates that high quality BCG signals can be collected in a home environment and used to detect the clinical state of HF patients. In future work, a clinician/caregiver can be introduced into the system so that appropriate interventions can be performed based on the clinical state monitored at home.

Automatic Detection of Atrial Fibrillation from Ballistocardiogram (BCG) Using Wavelet Features and Machine Learning.

This paper presents an unobtrusive method for automatic detection of atrial fibrillation (AF) from single-channel ballistocardiogram (BCG) recordings during sleep. We developed a remote data acquisition system that measures BCG signals through an electromechanical-film sensor embedded into a bed's mattress and transmits the BCG data to a remote database on the cloud server. In the feasibility study, 12 AF patients' data were recorded during entire night of sleep. Each BCG recording was split into nonoverlapping 30s epochs labeled either AF or normal. Using the features extracted from stationary wavelet transform of these epochs, three popular machine learning classifiers (support vector machine, K-nearest neighbor, and ensembles) have been trained and evaluated on the set of 7816 epochs employing 30% hold-out validation. The results showed that all the trained classifiers could achieve an accuracy rate above 91.5%. The optimized ensembles model (Bagged Trees) could achieve accuracy, sensitivity, and specificity of 0.944, 0.970 and 0.891, respectively. These results suggest that the proposed BCG-based AF detection can be a potential initial screening and detection tool of AF in home-monitoring applications.

Template-Based Statistical Modeling and Synthesis for Noise Analysis of Ballistocardiogram Signals: A Cycle-Averaged Approach.

Ballistocardiogram (BCG) can be recorded using inexpensive and non-invasive hardware to estimate physiological changes in the heart. In this paper, a methodology is developed to evaluate the impact of additive noise on the BCG signal. A statistical model is built that incorporates subject-specific BCG morphology. BCG signals segmented by electrocardiogram RR intervals (BCG heartbeats) are averaged to estimate a parent template and subtemplates leveraging the quasi-periodic nature of the heart. Noise statistics are obtained for subtemplates with respect to the parent template. Then, a synthesis algorithm with adjustable additive noise is devised to generate subtemplates based on the individual's parent template and statistics. For the example use of the synthesis algorithm, the average correlation coefficient between subtemplates and the parent template (subtemplate versus parent template approach) is tested as a signal quality index. A BCG heartbeat synthesis framework that incorporates an individual's BCG morphology and physiological variability was developed to quantify variations in the BCG signal against additive noise. The signal quality assessment of a person's BCG recording can be performed without requiring any a priori knowledge of the person's BCG morphology. A data-driven constraint on the required minimum number of heartbeats for a reliable template estimation was provided. The impact of additive noise on BCG morphology and estimated physiological parameters can be analyzed using the developed methodology without requiring prior statistics. This paper can facilitate the performance evaluation of BCG analysis algorithms against additive noise.

Towards precise tracking of electric-mechanical cardiac time intervals through joint ECG and BCG sensing and signal processing.

Automatic tracking of intra-beat cardiac activities in ballistocardiogram (BCG) is a highly interesting yet technically challenging topic for cardiac monitoring, due to the signal's high susceptibility to various forms of distortions. In this paper, we aim to further investigate the BCG waveform detection from a signal processing and analysis viewpoint. We collect synchronized electrocardiography(ECG) and BCG recordings from four healthy human subjects using an in-house built multi-physiological monitoring device. Particularly, we study post-exercise ECG-BCG signals that embed considerable variation in the heart beat during the post-exercise recovery phase. Furthermore, we develop an efficient and interactive tool for detecting and marking ECG-BCG waveforms in each heart beat. Through analyzing the detected time interval signals, we explore new interesting patterns of dynamic associations between different time interval signals. At the same time, we call for development of improved detection algorithms to address robustness and accuracy issues.

Central blood pressure monitoring method oriented to wearable devices

Arterial hypertension affects a quarter of the world’s population and is a major risk factor for cardiovascular disease. Blood pressure (BP) is one of the most relevant parameters used for continual monitoring of possible hypertension states in the elderly population. Furthermore, emerging evidence suggests that central blood pressure is a more accurate predictor of future cardiovascular events than brachial pressure, thus potentially providing a better hemodynamic determinant for clinical outcomes. This work presents a non-invasive Central (Aortic) blood pressure estimation method based on the pulse transit time (ptt) principle estimated from electrocardiogram (ECG) and ballistocardiogram (BCG) recordings. As the measured transit time presented takes place mainly within the aortic domain, we estimate aortic blood pressure instead of brachial pressure. Validation of the proposed method was performed with a small sample of healthy volunteers at a local gym. An Atcor Medical SphygmoCor device was used to monitor central and brachial blood pressure (systolic, mean, and diastolic) during rests of the strength maneuvers. Pulse transit time was estimated from ECG and BCG recordings and simultaneously recorded with all BP readings. Results showed that Systolic and Mean central blood pressures were most strongly correlated with ppt-estimated blood pressure in comparison to all other blood pressure readings; Bland-Altman plots showed an almost zero mean error (|μ|< 0.02mmHg) and bounded standard deviation σ< 5mmHg for all systolic and mean central BP readings. Experimental data, thus showed promissory results for monitoring aortic blood pressure via ptt. The scope of this pilot work is to provide initial validation of this method in order to develop a compact miniaturized device that allows the integration of wireless blood pressure monitoring into a wearable system.

Towards numerical temporal-frequency system modelling of associations between electrocardiogram and ballistocardiogram.

Ballistocardiogram (BCG) is a vital sign of ballistic forces generated by each heartbeat. With the advancements in related sensor and computing technologies in recent years, BCG has become far more accessible and thus regained its interest in both research and industry fields. Here we would like to promote the system modelling approach to BCG computing that allows to explore the underlying association between BCG and other physiological signals such as electrocardiogram (ECG). This is in contrast to most of the existing works in the related signal processing domain, which focus on detecting heart rate only. The system modelling approach may eventually improve the clinical significance of the BCG by extracting deeply embedded information. Towards this goal, here we present our preliminary study where we design a Wavelet-based temporal-frequency system model for associating BCG and ECG. To validate the model, we also collect simultaneous BCG and ECG recordings from 4 healthy subjects. We use the system model to build a BCG to ECG predicting algorithm. We demonstrate that this temporal-frequency model and algorithm is far superior, in terms of accuracy, to the naïve method of linear modelling.

A ballistocardiogram measurement system for home monitoring: design, performance, and evaluation

We present a ballistocardiogram (BCG) measurement system for obtaining the BCG signal. The performance of the BCG measurement system was tested, including the dynamic response frequency of the platform (32 Hz with a 75 kg load), and the signal to noise ratio of the system (minimum 23 dB). Factors that could influence BCG recording during measurement were investigated by collecting data from 48 healthy subjects (28 males and 20 females). The basic features of the BCG signal, such as the amplitude and time interval, were extracted, and differences by gender were analyzed. A correlation between the IJ amplitude (amplitude difference between I- and J-peak) and the respiration phase was found (R 2 = 0.49). The BCG signals for both sitting (SiBCG) and standing (StBCG) postures were obtained using this system. These showed that the StBCG amplitudes were larger than those of SiBCG but there was no significant difference in the time intervals. The robustness of our system was verified, and its potential for non-invasive cardiac activity monitoring was demonstrated.

Evaluating the Lower-Body Electromyogram Signal Acquired From the Feet As a Noise Reference for Standing Ballistocardiogram Measurements

The ballistocardiogram (BCG) is a measure of the reaction force of the body to cardiac ejection of blood. A variety of systems can be used for BCG detection, including beds, tables, chairs, and weighing scales. Weighing scales, in particular, have several practical advantages over the alternatives: low cost, small size, unobtrusiveness, and familiarity to the user; one disadvantage is that the subject must stand during the recording, rather than sit or lay supine, resulting in a higher susceptibility to motion artifacts in the measured signal. This paper evaluates the electromyogram (EMG) signal acquired from the feet of the subject during BCG recording as a noise reference for standing BCG measurements. As a subject moves while standing on the scale, muscle contractions in the feet are detected by the EMG signal, and used to flag segments of the BCG signal that are corrupted by elevated noise. For the purposes of evaluating this method, estimates of the BCG noise-to-signal ratio (NSR) were independently calculated with an ensemble average method, using the R-wave of a simultaneously-acquired chest ECG as a timing reference. The linear correlation between EMG power alone and BCG NSR from 14 subjects was found to be moderate ( r = 0.58, F-statistic p -value 0.05); combined with body-mass index (BMI), multiple linear regression yielded a stronger correlation ( r = 0.73, F -statistic p-value = 0.01). Additionally, an example usage of the lower-leg EMG for improving BCG measurement robustness is provided.

Adaptive Cancellation of Floor Vibrations in Standing Ballistocardiogram Measurements Using a Seismic Sensor as a Noise Reference

An adaptive noise canceller was used to reduce the effect of floor vibrations on ballistocardiogram (BCG) measurements from a modified electronic bathroom scale. A seismic sensor was placed next to the scale on the floor and used as the noise reference input to the noise canceller. BCG recordings were acquired from a healthy subject while another person stomped around the scale, thus causing increased floor vibrations. The noise canceller substantially eliminated the artifacts in the BCG signal due to these vibrations without distorting the morphology of the measured BCG. Additionally, recordings were obtained from another subject standing inside a parked bus while the engine was running. The artifacts due to the vibrations of the engine, and the other vehicles moving on the road next to the bus, were also effectively eliminated by the noise canceller. The system with automatic floor vibration cancellation could be used to increase BCG measurement robustness in home monitoring applications. Additionally, the noise cancellation approach may enable BCG recording in ambulances-or other transport vehicles-where noninvasive hemodynamic monitoring may otherwise not be feasible.

Simple, noninvasive system for measuring the heart rate of avian embryos and hatchlings by means of a piezoelectric film

Using a flexible piezoelectric film, the authors developed a simple system to determine noninvasively the heart rate of chicken embryos and hatchlings. The film was piezoelectric polyvinylidene fluoride (PVDF), which is sensitive enough to detect cardiogenic ballistic movements of the egg (ballistocardiogram (BCG)) and precordial movements of the hatchling attributable to cardiac contractions (apexcardiogram (ACG)). The BCG could be detected, during the second half of incubation, by placing the egg on the PVDF film on a soft substrate. The detected signal was found to be a measure of movement velocity. The ACG could be measured when the hatchling's chest wall made contact with the PVDF film installed in a box in which the hatchling was confined. The heart rate was counted from the lag time of autocorrelation calculated for a certain time segment (e.g. 2 s) of the BCG and ACG recordings.

Use of the SCSB method for monitoring of respiration, body movements and ballistocardiogram in infants

A new method for long-term monitoring of respiration, the ballistocardiogram (BCG), heart rate and body movements in newborn infants and children is presented. The design of the static charge-sensitive bed (SCSB) method for the monitoring of newborn infants and children is described. The method is simple and inexpensive. No electrodes or cables need to be connected to the subject. The SCSB method allows continuous long-term monitoring of body movements, respiratory movement, the BCG and heart rate. The results of about 40 neonatal recordings made in our clinic with the SCSB method demonstrate that recording body movements and respiration is possible in ordinary hospital wards. The infra-acoustic noise due to air-conditioning which is usually present in ordinary wards disturbs the BCG recordings in neonates. To obtain adequate BCG recordings this noise must be eliminated from recording rooms. Low-frequency noise is low enough to allow good BCG recordings in private dwellings. The SCSB method opens up new approaches to long-term studies of the regulation of respiration, body movements and the ballistocardiogram in newborn infants and children. Possible clinical applications of the SCSB method are discussed.

Bedside Technics for the Evaluation of the Heart Failure in Infants and Children

The microvibration (MV) on the scalp as a substitute for the conventional ballistocardiogram and electrocardiogram (ECG) of the limb lead II were simultaneously recorded on 120 healthy subjects and 26 patients with heart failure ranging in age from newborn to school children. The duration from the Q wave of the ECG to the J deflection of the MV was measured in seconds as the Q-J interval. The Q-J interval might be thought to represent the duration from the ventricular depolarization to the ejection period of the ventricular mechanical systole. In other words, it was the pre-ejection period added to mechanical ejection period. The prolongation in the Q-J interval correlated with age in healthy subjects being related inversely to the heart rate. In patients with heart failure, the Q-J interval prolonged. The prolongation of the Q-J interval correlated well with the myocardial contractile abnormality. This MV recording can be used for easy procedure in the bedside evaluation of cardiac performance in infants and children. Need for the investigation on cardiac performance in infants and children with congenital heart disease has been increased. Inspite of many noninvasive approaches, few useful expression of cardiac performance can be applied at the bedside in the pediatric field. Measurement of the systolic time intervals of left ventricular systole from simultaneous recordings of the electrocardiogram, the phonocardiogram and the carotid arterial pulsation determined the systolic phases of the cardiac cycle.1) These means are convenient detecting changes in cardiac performance for adults, but it is very difficult to record the carotid arterial pulsation in infants and children because of the constitutional problems. On the other hand, the systolic phases of the cardiac cycle were determined from simultaneous recordings of the electrocardiogram and the ballistocardiogram by Thompson2) in 1953. The ballistocardiogram recording had many problems to be solved. One of them was that the ballistocardiographic table was made for adults and the other was that the ballistocardiographic records were unsuitable for tachycardiac subjects such as infants. Because of the above mentioned reasons, the ballistocardiographic research did not progress in the pediatric field, 3)4)5)6) and the measurement of the wave of the ballistocardiogram was difficult at this age.

Ballistocardiogram recording in acute experiments on dogs

The construction of the ballistocardiogram described in this article provides reliable attachment to the movable part of the electromagnetic pickup on the dog's symphysis pubis. Thus the precise reproduction of the oscillations of its body is obtained. The operation which is required for this is very simple and does not take much time.