Ballistocardiogram Signals Research Articles

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.

The Segmented Dynamic Time Warping Algorithm for Beat-to-Beat Heart Rate Estimation based on Ballistocardiogram Signals

The Segmented Dynamic Time Warping Algorithm for Beat-to-Beat Heart Rate Estimation based on Ballistocardiogram Signals

Noninvasive Measurement of Physiological Signals on a Modified Home Bathroom Scale

A commercial bathroom scale with both handlebar and footpad electrodes was modified to enable measurement of four physiological signals: the ballistocardiogram (BCG), electrocardiogram (ECG), lower body impedance plethysmogram (IPG), and lower body electromyogram (EMG). The BCG, which describes the reaction of the body to cardiac ejection of blood, was measured using the strain gauges in the scale. The ECG was detected using handlebar electrodes with a two-electrode amplifier. For the lower body IPG, the two electrodes under the subject's toes were driven with an ac current stimulus, and the resulting differential voltage across the heels was measured and demodulated synchronously with the source. The voltage signal from the same two footpad electrodes under the heels was passed through a passive low-pass filter network into another amplifier, and the output was the lower body EMG signal. The signals were measured from nine healthy subjects, and the average signal-to-noise ratio (SNR) while the subjects were standing still was estimated for the four signals as follows: BCG, 7.6 dB; ECG, 15.8 dB; IPG, 10.7 dB. During periods of motion, the decrease in SNR for the BCG signal was found to be correlated to the increase in rms power for the lower body EMG (r = 0.89, p <; 0.01). The EMG could, thus, be used to flag noise-corrupted segments of the BCG, increasing the measurement robustness. This setup could be used for monitoring the cardiovascular health of patients at home.

Ballistocardiogram Insusceptibility Detection and Analysis System on FPGA

Patients with heart disease or undergoing therapy at home are in a desperate need of detecting ECG signals under normal living conditions for a long time. However, traditional ECG detecting systems require electrode contacting with the human body. The research in this paper is concerned with the development of a novel Ballistocardiogram(BCG) detection and processing system on FPGA without attaching any electrodes to the subject for monitoring health statues. The proposed system consists of two modules: a suitable BCG signal acquisition module, and a BCG signal processing circuit on FPGA accomplished for J-wave detection and heart rate calculating. To ensure the accuracy of the experiment, ECG signal and BCG signal were sampled at the same time, and the value of J-J intervals and R-R intervals were compared. An adaptive threshold method is implemented for J-wave detection on FPGA.

Acquisition and Analysis System of the Ballistocardiogram Signal Based on Virtual Instruments

Ballistocardiogram signal (BCG) is a non-invasive technique for the assessment of the cardiac function. It consists mainly of heart movement and the movement of blood in aorta, arteries, and periphery, which can be used to real-time monitor the heart rate and respiration frequency at home. In our laboratory, a sitting BCG detection chair has been designed successfully, and the acquisition and analysis system based on virtual instruments is proposed in this paper. MATLAB7.0 and LabVIEW8.5 were used to simulate the operational environment, and the results show high efficiency and accuracy in displaying waveform and spectrum, extracting main characteristics of heart rate and respiratory frequency, and alerting when abnormal heart-rate occurs.

Heart rate variability analysis using a ballistocardiogram during Valsalva manoeuvre and post exercise

We introduced a novel non-constrained technique for estimating heart rate variability (HRV) using a ballistocardiogram (BCG). To assess whether the BCG signal can be used to analyse the cardiac autonomic modulation, HRV parameters derived from the BCG signal (ballistocardiographic HRV, B-HRV) were statistically compared with the HRV parameters from the ECG signal during rest and under two different experimental conditions that induce cardiac autonomic rhythm changes: the Valsalva manoeuvre and static exercise. Time domain, frequency domain and nonlinear analyses were individually performed on 15 healthy subjects to assess whether the BCG can be used to analyse the cardiac autonomic modulation under each condition. For all subjects, the proposed method had averages of relative errors of 5.01 ± 4.72, 5.64 ± 4.83 and 5.98 ± 5.80% for resting, Valsalva and post-exercise sessions, respectively, and the correlation coefficients between the reference (ECG) and proposed (BCG) methods are 0.97, 0.98 and 0.98, for resting, Valsalva and post-exercise sessions, respectively. During cardiac autonomic changes, the B-HRV parameters changed in a pattern that is very similar to the variations in the HRV parameters based on Student's t-test results. In addition, some of the B-HRV parameters changed according to cardiac autonomic rhythms controlled by sympathetic and parasympathetic activities during the experiments. These findings indicate that BCG can provide an accurate and reliable means to evaluate autonomic system activation by HRV in its unconstrained way.

A low-noise ac-bridge amplifier for ballistocardiogram measurement on an electronic weighing scale

Ballistocardiography is a non-invasive technique for evaluating cardiovascular health. This note presents an ac-bridge amplifier for low-noise ballistocardiogram (BCG) recording from a modified weighing scale. The strain gauges in a commercial scale were excited by an ac source—square or sine wave—and the differential output voltage resulting from the BCG was amplified and demodulated synchronously with the excitation waveform. A standard BCG amplifier, with a simple dc-bridge excitation, was also built and the performance was compared to both the square- and sine-wave excited ac-bridge amplifiers. The total input-referred voltage noise (rms) integrated over the relevant BCG bandwidth of 0.3–10 Hz was found to be 30 nV (square wave source) or 25 nV (sine-wave source) for the ac-bridge amplifier and 52 nV for the standard amplifier: an improvement of 4.8 dB or 6 dB, respectively. These correspond to input-referred force noise (rms) values of 5 mN, 4 mN and 8.3 mN. The improvement in SNR was also observed in recorded waveforms from a seated subject whose BCG signal was measured with both dc- and ac-bridge circuits.

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.

Robust ballistocardiogram acquisition for home monitoring

The ballistocardiogram (BCG) measures the reaction of the body to cardiac ejection forces, and is an effective, non-invasive means of evaluating cardiovascular function. A simple, robust method is presented for acquiring high-quality, repeatable BCG signals from a modified, commercially available scale. The measured BCG waveforms for all subjects qualitatively matched values in the existing literature and physiologic expectations in terms of timing and IJ amplitude. Additionally, the BCG IJ amplitude was shown to be correlated with diastolic filling time for a subject with premature atrial contractions, demonstrating the sensitivity of the apparatus to beat-by-beat hemodynamic changes. The signal-to-noise ratio (SNR) of the BCG was estimated using two methods, and the average SNR over all subjects was greater than 12 for both estimates. The BCG measurement was shown to be repeatable over 50 recordings taken from the same subject over a three week period. This approach could allow patients at home to monitor trends in cardiovascular health.

An Electromechanical Film Sensor Based Wireless Ballistocardiographic Chair: Implementation and Performance

New sensor technologies open possibilities for measuring traditional biosignals in new innovative ways. This, together with the development of signal processing systems and their increasing computing power, can sometimes give new life to old measurement techniques. Ballistocardiogram (BCG) is one such technique, originally promising but later replaced by the now very popular electrocardiogram. It's usability was previously limited by the large size of the devices required to record it, and the complex nature of the recorded signal, which gave little information in visual inspection. In this paper, we present how a lightweight and flexible electromechanical film (EMFi) sensor can be used to record BCG. A ballistocardiographic chair, designed to look like a normal office chair, was built and fitted with two sensitive EMFi sensors. Two different measurement setups to record the signal from the EMFi sensors were developed. The first, so-called wired setup, uses a commercial bio-amplifier, and a special pre-amplifier to interface to it. The latter, so-called wireless setup, uses our own hardware to transmit the recorded digitized signals wirelessly to a nearby PC. Both of these systems are presented and their performance evaluated. Also, the suitability, limitations and advantages of the EMFi sensor over existing sensors and methods are discussed. The validity of the EMFi sensor and amplifier output is tested using a mechanical vibrator. Lastly, a summary of signal analysis methods developed for our system is given. The developed systems have be used for medical BCG measurements, and the recordings indicate that the both the systems are functional and capture useful BCG signal components.

Improvement of ballistocardiogram processing by inclusion of respiration information

In this paper a novel methodology for processing of a ballistocardiogram (BCG) is proposed in which the respiration signal is utilized to improve the averaging of the BCG signal and ultimately the annotation and interpretation of the signal. Previous research works filtered out the respiration signal while the novelty of the current research is that, rather than removing the respiration effect from the signal, we utilize the respiration information to improve the averaging and thus analysis and interpretation of the BCG signal in diagnosis of cardiac malfunctions. This methodology is based on our investigation that BCG cycles corresponding to the inspiration and expiration phases of the respiration cycle are different in morphology. BCG cycles corresponding to the expiration phase of respiration have been proved to be more closely related to each other when compared to cycles corresponding to inspiration, and therefore expiration cycles are better candidates to be selected for the calculation of the averaged BCG signal. The new BCG average calculated based on this methodology is then considered as the representative and a template of the BCG signal for further processing. This template can be considered as the output of a clinical BCG instrument with higher reliability and accuracy compared to the previous processing methods.

An Intelligent Ballistocardiographic Chair using a Novel SF-ART Neural Network and Biorthogonal Wavelets

This paper presents a comparative analysis of novel supervised fuzzy adaptive resonance theory (SF-ART), multilayer perceptron (MLP) and Multi Layer Perceptrons (MLP) neural networks over Ballistocardiogram (BCG) signal recognition. To extract essential features of the BCG signal, we applied Biorthogonal wavelets. SF-ART performs classification on two levels. At first level, pre-classifier which is self-organized fuzzy ART tuned for fast learning classifies the input data roughly to arbitrary (M) classes. At the second level, post-classification level, a special array called Affine Look-up Table (ALT) with M elements stores the labels of corresponding input samples in the address equal to the index of fuzzy ART winner. However, in running (testing) mode, the content of an ALT cell with address equal to the index of fuzzy ART winner output will be read. The read value declares the final class that input data belongs to. In this paper, we used two well-known patterns (IRIS and Vowel data) and a medical application (Ballistocardiogram data) to evaluate and check SF-ART stability, reliability, learning speed and computational load. Initial tests with BCG from six subjects (both healthy and unhealthy people) indicate that the SF-ART is capable to perform with a high classification performance, high learning speed (elapsed time for learning around half second), and very low computational load compared to the well-known neural networks such as MLP which needs minutes to learn the training material. Moreover, to extract essential features of the BCG signal, we applied Biorthogonal wavelets. The applied wavelet transform requires no prior knowledge of the statistical distribution of data samples.