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Purpose: This study aimed to develop an unobtrusive method for home sleep apnea testing (HSAT) utilizing micromotion signals obtained by a piezoelectric rubber sheet sensor., Methods: Algorithms were designated to extract respiratory and ballistocardiogram components from micromotion signals and to detect respiratory events as the characteristic separation of the fast envelope of the respiration component from the slow envelope. In 78 adults with diagnosed or suspected sleep apnea, micromotion signal was recorded with a piezoelectric rubber sheet sensor placed beneath the bedsheet during polysomnography. In a half of the subjects, the algorithms were optimized to calculate respiratory event index (REI), estimating apnea-hypopnea index (AHI). In the other half of subjects, the performance of REI in classifying sleep apnea severity was evaluated. Additionally, the predictive value of the frequency of cyclic variation in heart rate (Fcv) obtained from the ballistocardiogram was assessed., Results: In the training group, the optimized REI showed a strong correlation with the AHI (r = 0.93). Using the optimal cutoff of REI ≥ 14/h, subjects with an AHI ≥ 15 were identified with 77.8% sensitivity and 90.5% specificity. When applying this REI to the test group, it correlated closely with the AHI (r = 0.92) and identified subjects with an AHI ≥ 15 with 87.5% sensitivity and 91.3% specificity. While Fcv showed a modest correlation with AHI (r = 0.46 and 0.66 in the training and test groups), it lacked independent predictive power for AHI., Conclusion: The analysis of respiratory component of micromotion using piezoelectric rubber sheet sensors presents a promising approach for HSAT, providing a practical and effective means of estimating sleep apnea severity., (© 2024. The Author(s).)

Recent studies have shown that head movements associated with cardiac activity contain a heart rate (HR) signal. In most previous studies, subjects were required to remain stationary in a specific environment during HR measurements, and measurement accuracy depended on the choice of target in the scene, i.e., the specified region of the face. In this paper, we proposed a robust HR measurement method based on ballistocardiogram (BCG) technology. This method requires only a camera and does not require that users establish a complex measurement environment. In addition, a bidirectional optical flow algorithm is designed to select and track valid feature points in the video captured by using the camera. Experiments with 11 subjects show that the HR values measured using the proposed method differ slightly from the reference values, and the average error is only 1.09%. Overall, this method can improve the accuracy of BCG without limitations related to skin tone, illumination, the state of the subject, or the test location.

Evidence suggests that central aortic blood pressure (CABP) may provide a more accurate prognosis of cardiovascular events than peripheral pressure. The capability of monitoring CABP in a continuous, wearable, unobtrusive way might have a significant impact on hypertension management. The purpose of this study is to experimentally explore whether a wearable device equipped with an electrocardiogram (ECG) and ballistocardiogram (BCG) acquisition system could be used to predict CABP. This is based on state-of-the-art results on the relationship between transit time extracted from these signals and CABP. Ten young, healthy volunteers participated in the study where data-sets were acquired during three hemodynamic interventions, i.e., breath-holding, Valsalva maneuver, and cold pressor. Each data-set included ECG and BCG waveforms acquired by the wearable device and a CABP assessment from a cuff-based device. A total of nine PTT-based models (PBMs) derived from pulse transit time methodology were considered. Each PBM was tested with three alternative feature times extracted from the recorded waveforms PBMs were calibrated with data-sets acquired at baseline state, which were not considered for testing the PBM estimation performance. Four of the nine tested models presented a proper agreement in estimating CABP through the acquired signals, after the calibration procedure with baseline-state data. Results in one of these promising models are the following. Mean estimation error (95% confidence interval), systolic: 0 to 1.7 mmHg, diastolic: 0.4 to 2.3 mmHg, Pearson correlation: 0.82 systolic and 0.78 diastolic (p < 0.001). The proposed methodology may lead to continuous wearable BP monitoring.

This paper presents a ballistocardiogram (BCG) based beat-to-beat heart rate (HR) measurement system for healthcare applications in smart home. The BCG signal is picked up by two parallel connected piezoelectric elements, and processed by a robust HR detection algorithm. To deal with the diversity in BCG signal, a personalized heartbeat template is extracted to handle the diversity in BCG signal. The Pearson correlation coefficient is used to correlate between personalized template and the BCG signal. Heartbeats are detected by using the Hilbert Transform. The system is verified on 32 subjects with simultaneously recorded BCG and ECG signals. With the reference to ECG signal, we have achieved mean absolute relative error of the beat-to-beat intervals of 4.56%, and average absolute error ±standard deviation of absolute error for HR estimation in a 30-seconds window of 0.59 ±0.56 beats per minute. Meanwhile, 99.67% of the beat-to-beat intervals are detected on average.

There exists a technological momentum towards the development of unobtrusive, simple, and reliable systems for long-term sleep monitoring. An off-the-shelf commercial pressure sensor meeting these requirements is the Emfit QS. First, the potential for sleep apnea screening was investigated by revealing clusters of contaminated and clean segments. A relationship between the irregularity of the data and the sleep apnea severity class was observed, which was valuable for screening (sensitivity 0.72, specificity 0.70), although the linear relation was limited ( R 2 of 0.16). Secondly, the study explored the suitability of this commercial sensor to be merged with gold standard polysomnography data for future sleep monitoring. As polysomnography (PSG) and Emfit signals originate from different types of sensor modalities, they cannot be regarded as strictly coupled. Therefore, an automated synchronization procedure based on artefact patterns was developed. Additionally, the optimal position of the Emfit for capturing respiratory and cardiac information similar to the PSG was identified, resulting in a position as close as possible to the thorax. The proposed approach demonstrated the potential for unobtrusive screening of sleep apnea patients at home. Furthermore, the synchronization framework enabled supervised analysis of the commercial Emfit sensor for future sleep monitoring, which can be extended to other multi-modal systems that record movements during sleep.

Hypertension is one of the most common cardiovascular diseases, which will cause severe complications if not treated in a timely way. Early and accurate identification of hypertension is essential to prevent the condition from deteriorating further. As a kind of complex physiological state, hypertension is hard to characterize accurately. However, most existing hypertension identification methods usually extract features only from limited aspects such as the time-frequency domain or non-linear domain. It is difficult for them to characterize hypertension patterns comprehensively, which results in limited identification performance. Furthermore, existing methods can only determine whether the subjects suffer from hypertension, but they cannot give additional useful information about the patients' condition. For example, their classification results cannot explain why the subjects are hypertensive, which is not conducive to further analyzing the patient's condition. To this end, this paper proposes a novel hypertension identification method by integrating classification and association rule mining. Its core idea is to exploit the association relationship among multi-dimension features to distinguish hypertensive patients from normotensive subjects. In particular, the proposed method can not only identify hypertension accurately, but also generate a set of class association rules (CARs). The CARs are proved to be able to reflect the subject's physiological status. Experimental results based on a real dataset indicate that the proposed method outperforms two state-of-the-art methods and three common classifiers, and achieves 84.4%, 82.5% and 85.3% in terms of accuracy, precision and recall, respectively.

We propose a nonwearable hydraulic bed sensor system that is placed underneath the mattress to estimate the relative systolic blood pressure of a subject, which only differs from the actual blood pressure by a scaling and an offset factor. Two types of features are proposed to obtain the relative blood pressure, one based on the strength and the other on the morphology of the bed sensor ballistocardiogram pulses. The relative blood pressure is related to the actual by a scale and an offset factor that can be obtained through calibration. The proposed system is able to extract the relative blood pressure more accurately with a less sophisticated sensor system compared to those from the literature. We tested the system using a dataset collected from 48 subjects right after active exercises. Comparison with the ground truth obtained from the blood pressure cuff validates the promising performance of the proposed system, where the mean correlation between the estimate and the ground truth is near to 90% for the strength feature and 83% for the morphology feature.

Home monitoring requires measuring the physiological and behavioral signals without impairing a subject's everyday life. This paper presents an integrated and noncontact approach for obtaining simultaneous physiological and behavioral signals of recumbent humans in beds using a home-monitoring application. In the proposed approach, a fabric-sheet unified sensing electrode (FUSE) obtains physiological signals by recording the electrocardiogram (ECG), chest and abdominal respiratory movements (RMs), and ballistocardiogram (BCG). The FUSE also detects the behavioral signals of body proximity (BPx) and lateral/supine lying postures. A prototype system with FUSE was validated in a short-term experiment and 6-h overnight measurements on two different groups composed of seven lying subjects. The results confirmed that the approach senses each signal independently and records the ECG, RMs, BCG, and BPx signals simultaneously. The mean sensitivities of the R and T waves of the ECG during sleep were 86.1% and 88.0%, respectively, whereas those of the chest and abdominal RMs were 90.7% and 90.1%, respectively. Although our prototype system has room for improvement, the results suggest that our approach enables the unconstrained, nocturnal monitoring of the physiological and behavioral signals in recumbent humans. The at-home monitoring of the physiological and behavioral signals is expected to contribute to cost-effective personalized healthcare in the future. This noncontact and easy-to-install system for in-bed measurements can facilitate a new era of home monitoring.

Several devices and measurement approaches have recently been developed to perform ballistocardiogram (BCG) and seismocardiogram (SCG) measurements. The development of a wireless acquisition system (hardware and software), incorporating a novel high-resolution micro-electro-mechanical system (MEMS) accelerometer for SCG and BCG signals acquisition and data treatment is presented in this paper. A small accelerometer, with a sensitivity of up to 0.164 µs/µg and a noise density below 6.5 µg/ Hz is presented and used in a wireless acquisition system for BCG and SCG measurement applications. The wireless acquisition system also incorporates electrocardiogram (ECG) signals acquisition, and the developed software enables the real-time acquisition and visualization of SCG and ECG signals (sensor positioned on chest). It then calculates metrics related to cardiac performance as well as the correlation of data from previously performed sessions with echocardiogram (ECHO) parameters. A preliminarily clinical study of over 22 subjects (including healthy subjects and cardiovascular patients) was performed to test the capability of the developed system. Data correlation between this measurement system and echocardiogram exams is also performed. The high resolution of the MEMS accelerometer used provides a better signal for SCG wave recognition, enabling a more consistent study of the diagnostic capability of this technique in clinical analysis.

A wearable multi-sensor system allowing synchronized unobtrusive measurements of 4 vital signs at a dedicated location of interest is presented. The 4×U sensor is capable of synchronously measuring magnetic impedance, reflective photoplethysmography, capacitive electrocardiogram and seismocardiography (ballistocardiography). The hardware of all modalities is described and some preliminary results are reported.

This study demonstrates the feasibility of using a seat sensor designed for occupant classification from a production passenger vehicle to measure an occupant&rsquo;s respiration rate (RR) and heart rate (HR) in a laboratory setting. Relaying occupant vital signs after a crash could improve emergency response by adding a direct measure of the occupant state to an Advanced Automatic Collision Notification (AACN) system. Data was collected from eleven participants with body weights ranging from 42 to 91 kg using a Ford Mustang passenger seat and seat sensor. Using a ballistocardiography (BCG) approach, the data was processed by time domain filtering and frequency domain analysis using the fast Fourier transform to yield RR and HR in a 1-min sliding window. Resting rates over the 30-min data collection and continuous RR and HR signals were compared to laboratory physiological instruments using the Bland-Altman approach. Differences between the seat sensor and reference sensor were within 5 breaths per minute for resting RR and within 15 beats per minute for resting HR. The time series comparisons for RR and HR were promising with the frequency analysis technique outperforming the peak detection technique. However, future work is necessary for more accurate and reliable real-time monitoring of RR and HR outside the laboratory setting., Competing Interests: The authors declare no conflict of interest.

Cardiovascular diseases (CVDs) are prevalent in the US, and many forms of CVD primarily affect the mechanical aspects of heart function. Wearable technologies for monitoring the mechanical health of the heart and vasculature could enable proactive management of CVDs through titration of care based on physiological status as well as preventative wellness monitoring to help promote lifestyle choices that reduce the overall risk of developing CVDs. Additionally, such wearable technologies could be used to optimize human performance in austere environments. This review describes our progress in developing wearable ballistocardiogram (BCG)- and seismocardiogram-based systems for monitoring relative changes in cardiac output, contractility, and blood pressure. Our systems use miniature, low-noise accelerometers to measure the movements of the body in response to the heartbeat and novel machine learning algorithms to provide robustness against motion artifacts and sensor misplacement. Moreover, we have mathematically related wearable BCG signals-representing local, cardiogenic movements of a point on the body-to better understood whole body BCG signals, and thereby improved estimation of key health parameters. We validated these systems with experiments in healthy subjects, studies in patients with heart failure, and measurements in austere environments such as water immersion. The systems can be used in future work as a tool for clinicians and physiologists to measure the mechanical aspects of cardiovascular function outside of clinical settings, and to thereby titrate care for patients with CVDs, provide preventative screening, and optimize performance in austere environments by providing real-time in-depth information regarding performance and risk.

Background: Measurement of arterial stiffness should be more available. Our aim was to show that aortic pulse wave velocity can be reliably measured with a bathroom scale combining the principles of ballistocardiography (BCG) and impedance plethysmography on a single foot., Method: The calibration of the bathroom scale was conducted on a group of 106 individuals. The aortic pulse wave velocity was measured with the SphygmoCor in the supine position. Three consecutive measurements were then performed on the Withings scale in the standing position. This aorta-leg pulse transit time (alPTT) was then converted into a velocity with the additional input of the height of the person. Agreement between the SphygmoCor and the bathroom scale so calibrated is assessed on a separate group of 86 individuals, following the same protocol., Results: The bias is 0.25 m·s-1 and the SE 1.39 m·s-1. This agreement with Sphygmocor is "acceptable" according to the ARTERY classification. The alPTT correlated well with cfPTT with (Spearman) R = 0.73 in pooled population (cal 0.79, val 0.66). The aorta-leg pulse wave velocity correlated with carotid-femoral pulse wave velocity with R = 0.76 (cal 0.80, val 0.70)., Conclusion: Estimation of the aortic pulse wave velocity is feasible with a bathroom scale. Further investigations are needed to improve the repeatability of measurements and to test their accuracy in different populations and conditions., (© The Author 2017. Published by Oxford University Press on behalf of American Journal of Hypertension.)

For more than a century, it has been known that the body recoils each time the heart ejects blood into the arteries. These subtle cardiogenic body movements have been measured with increasingly convenient ballistocardiography (BCG) instruments over the years. A typical BCG measurement shows several waves, most notably the "I", "J", and "K" waves. However, the mechanism for the genesis of these waves has remained elusive. We formulated a simple mathematical model of the BCG waveform. We showed that the model could predict the BCG waves as well as physiologic timings and amplitudes of the major waves. The validated model reveals that the principal mechanism for the genesis of the BCG waves is blood pressure gradients in the ascending and descending aorta. This new mechanistic insight may be exploited to allow BCG to realize its potential for unobtrusive monitoring and diagnosis of cardiovascular health and disease.

Background: SonR sensor signal correlates well with myocardial contractility expressed in terms of left ventricular (LV) dP/dt max. The aim of our study was to evaluate the changes in myocardial contractility during isometric effort in heart failure patients undergoing cardiac resynchronization therapy (CRT) with right atrial SonR sensor., Methods: Thirty-one patients (19 men, 65 ± 7 years, LV ejection fraction [LVEF] 28% ± 5%, in sinus rhythm) were implanted with a CRT-defibrillator (CRT-D) device equipped with SonR sensor, which was programmed in VVI mode at 40 beats/min. Twenty-four hours after implantation, each patient underwent a noninvasive hemodynamic evaluation at rest and during isometric effort, including: (1) measurement of beat-to-beat endocavitary SonR signal; (2) echocardiographic assessment; and (3) continuous measurement of blood pressure with Nexfin method (BMEYE, Amsterdam, the Netherlands). The following contractility parameters were considered: (1) mean value of beat-to-beat SonR signal; (2) mean value of LV dP/dt by Nexfin system; and (3) fractional shortening (FS) by echocardiography., Results: At the third minute of the isometric effort, mean value of SonR signal significantly increased from baseline (P < 0.001). Similarly, mean value of both LV dP/dt by Nexfin and FS significantly increased compared to the resting condition (P < 0.001; P < 0.001). While in 27 (88%) patients SonR signal increased at the third minute of the isometric effort, in four (12%) patients SonR signal decreased. In these patients, both LV dP/dt by Nexfin and FS consensually decreased., Conclusions: In CRT patients, SonR sensor is able to detect changes in myocardial contractility in a consensual way like noninvasive methods such as Nexfin system and echocardiography., (© 2015 Wiley Periodicals, Inc.)

Background: Nocturnal hypoxemia, characterized by abnormally low oxygen saturation levels in arterial blood during sleep, is a significant feature of various pathological conditions. The oxygen desaturation index, commonly used to evaluate the nocturnal hypoxemia severity, is acquired using nocturnal pulse oximetry that requires the overnight wear of a pulse oximeter probe., Objectives: This study aimed to suggest a method for the unconstrained estimation of the oxygen desaturation index., Methods: We hypothesized that the severity of nocturnal hypoxemia would be positively associated with cardiac sympathetic activation during sleep. Unconstrained heart rate variability monitoring was conducted using three different ballistocardiographic systems to assess cardiac sympathetic activity. Overnight polysomnographic and ballistocardiographic recording pairs were collected from the 20 non-nocturnal hypoxemia (oxygen desaturation index <5 events/h) subjects and the 76 nocturnal hypoxemia patients. Among the 96 recording pairs, 48 were used as training data and the remaining 48 as test data., Results: The regression analysis, performed using the low-frequency component of heart rate variability, exhibited a root mean square error of 3.33 events/h between the estimates and the reference values of the oxygen desaturation index. The nocturnal hypoxemia diagnostic performance produced by our method was presented with an average accuracy of 96.5% at oxygen desaturation index cutoffs of ≥5, 15, and 30 events/h., Conclusions: Our method has the potential to serve as a complementary measure against the accidental slip-out of a pulse oximeter probe during nocturnal pulse oximetry. The independent application of our method could facilitate home-based long-term oxygen desaturation index monitoring., (© 2016 S. Karger AG, Basel.)

Ballistocardiography (BCG) measures vibrations of the body caused by ejection of blood from the heart, and the root mean square (RMS) of BCG measured with a weighing scale trends with cardiac output. However, BCG underwater has not been studied. Head-to-foot BCG signals were recorded with an accelerometer on the sternum of three human subjects. The heartbeats were clearly visible in the signals recorded underwater, and the resting change in RMS BCG was +360 μg (+36%) from air to cold water immersion (27.8 °C) while standing. This is within the 32%-62% increase in cardiac output observed in previous head-out immersion studies.

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 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 has 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.

Respiratory and heart failure are conditions that can occur with little warning and may also be difficult to predict. Therefore continuous monitoring of these bio-signals is advantageous for ensuring human health. The car safety belt is mainly designed to secure the occupants of the vehicle in the event of an accident. In the current research a prototype safety belt is developed, which is used to acquire respiratory and heart signals, under laboratory conditions. The current safety belt is constructed using a copper ink based nonwoven material, which works based on the piezo-resistive effect due to the pressure exerted on the sensor as a result of expansion of the thorax/abdomen area of the body for respiration and due to the principle of ballistocardiography (BCG) in heart signal sensing. In this research, the development of a theoretical model to qualitatively describe the piezo-resistive material is also presented in order to predict the relative change in the resistance of the piezo-resistive material due to the pressure applied.

Background: Seismocardiography is the noninvasive measurement of cardiac vibrations transmitted to the chest wall by the heart during its movement. While most applications for seismocardiography are based on unidirectional acceleration measurement, several studies have highlighted the importance of three-dimensional measurements in cardiac vibration studies. One of the main challenges in using three-dimensional measurements in seismocardiography is the significant inter-subject variability of waveforms. This study investigates the feasibility of using a unified frame of reference to improve the inter-subject variability of seismocardiographic waveforms., Methods: Three-dimensional seismocardiography signals were acquired from ten healthy subjects to test the feasibility of the present method for improving inter-subject variability of three-dimensional seismocardiograms. The first frame of reference candidate was the orientation of the line connecting the points representing mitral valve closure and aortic valve opening in seismocardiograms. The second candidate was the orientation of the line connecting the two most distant points in the three dimensional seismocardiogram. The unification of the frame of reference was performed by rotating each subject's three-dimensional seismocardiograms so that the lines connecting the desired features were parallel between subjects., Results: The morphology of the three-dimensional seismocardiograms varied strongly from subject to subject. Fixing the frame of reference to the line connecting the MC and AO peaks enhanced the correlation between the subjects in the y axis from 0.42 ± 0.30 to 0.83 ± 0.14. The mean correlation calculated from all axes increased from 0.56 ± 0.26 to 0.71 ± 0.24 using the line connecting the mitral valve closure and aortic valve opening as the frame of reference. When the line connecting the two most distant points was used as a frame of reference, the correlation improved to 0.60 ± 0.22., Conclusions: The results indicate that using a unified frame of reference is a promising method for improving the inter-subject variability of three-dimensional seismocardiograms. Also, it is observed that three-dimensional seismocardiograms seem to have latent inter-subject similarities, which are feasible to be revealed. Because the projections of the cardiac vibrations on the measurement axes differ significantly, it seems obligatory to use three-dimensional measurements when seismocardiogram analysis is based on waveform morphology.

We propose a simple and robust method to detect heartbeats using the ballistocardiogram (BCG) signal that is produced by a hydraulic bed sensor placed under the mattress. The proposed method is found beneficial especially when the BCG signal does not display consistent J-peaks, which can often be the case for overnight, in-home monitoring, especially with frail seniors. Heartbeat detection is based on the short-time energy of the BCG signal. Compared with previous methods that rely on the J-peaks observed from the BCG amplitude, we are able to achieve considerable improvement even when significant distortions are present. Test results are included for different BCG waveform patterns from older adults.

Systolic time intervals (STI) are non-invasive measures of cardiac function. Due to the fact that STI can be measured noninvasively outside the clinic, STI are a promising method for long-term monitoring of patients with cardiovascular disease. In particular, the pre-ejection period (PEP) has been measured successfully from body vibrations of the beating heart, a technique called ballistocardiography (BCG), using a weighing scale. Similar measurements can be made with on-body accelerometers, however these wearable BCG signals are typically more challenging to interpret than whole-body BCG. In this paper, we conducted a small pilot study with four subjects to investigate whether a body sensor network of four accelerometers positioned on the wrist, arm, sternum, and head could improve beat-by-beat PEP prediction beyond that of each sensor alone. Linear models were fitted from the R-J and R-I intervals of the four BCG signals to PEP measured with impedance cardiography from 5-minute recordings after isometric lower-body exercise. Specifically, we found that (i) the RMS error of PEP estimation from the wearable BCG sensors can be reduced by using double integration, (ii) the standard deviation of PEP estimates from R-I intervals was smaller than from R-J intervals, and (iii) linear models combining both R-J and R-I measurements from all sensors resulted in the best average correlation (r(2) = 0.96 ± 0.01) and lowest average root mean square error (2.5 ± 0.8 ms) from 5×2-fold cross validation.

The aim of this paper is to present and evaluate algorithms for heartbeat interval estimation from multiple spatially distributed force sensors integrated into a bed. Moreover, the benefit of using multichannel systems as opposed to a single sensor is investigated. While it might seem intuitive that multiple channels are superior to a single channel, the main challenge lies in finding suitable methods to actually leverage this potential. To this end, two algorithms for heart rate estimation from multichannel vibration signals are presented and compared against a single-channel sensing solution. The first method operates by analyzing the cepstrum computed from the average spectra of the individual channels, while the second method applies Bayesian fusion to three interval estimators, such as the autocorrelation, which are applied to each channel. This evaluation is based on 28 night-long sleep lab recordings during which an eight-channel polyvinylidene fluoride-based sensor array was used to acquire cardiac vibration signals. The recruited patients suffered from different sleep disorders of varying severity. From the sensor array data, a virtual single-channel signal was also derived for comparison by averaging the channels. The single-channel results achieved a beat-to-beat interval error of 2.2% with a coverage (i.e., percentage of the recording which could be analyzed) of 68.7%. In comparison, the best multichannel results attained a mean error and coverage of 1.0% and 81.0%, respectively. These results present statistically significant improvements of both metrics over the single-channel results (p < 0.05).

Provided an overview of materials on the history of the formation method ballistocardiography for diagnosis of cardiovascular disease, an assessment of its current status and prospects for further development. Investigation of the physiological changes in the myocardium by recording ballistocardiogram acquires greater relevance since the existing knowledge in the field of cardiac diagnosis were obtained using instruments that do not meet modern requirements. Although the results of previous studies and define the basic characteristics ballistocardiography nonetheless update and clarification of details in relation to the data obtained by the use of modern methods of measuring contractility of the myocardium, it is useful. The resumption of work on the development of reliable devices for ballistocardiography is an urgent task. Method ballistocardiography under military medicine may be used for the integrated assessment of the functional state of the cardiovascular system during clinical examination of servicemen during mass examinations of recruits, the selection of students in military schools, as well as in monitoring the hemodynamic status in a hospital.

In the project "Individualized Biomonitoring in Heart Failure (Biomon-HF)," innovative sensors and algorithms for measuring vital signs, i.e., during the nocturnal sleep period, have been developed and successfully tested in five clinical feasibility studies involving 115 patients. The Biomon-HF sensor concepts are an important step toward future patient-customized telemonitoring and sensor-guided therapy management in chronic heart failure, including early detection of upcoming HF exacerbation and comorbidities at home. The resulting preventable disease complications and emergencies and reduction of consequences of disease are very important advantages for the patients, causing relief for medical staff and, thus, offer an enormous potential for improvements and cost savings in healthcare systems.

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.

Fragmented sleep due to frequent awakenings represents a major cause of impaired daytime performance and adverse health outcomes. Currently, the gold standard for studying and assessing sleep fragmentation is polysomnography (PSG). Here, we propose an alternative method for real-time detection of nocturnal awakening via ballistocardiography using an unobtrusive polyvinylidene fluoride (PVDF) film sensor on a bed mattress. From ballistocardiogram, heart rate and body movement information were extracted to develop an algorithm for classifying sleeping and awakening epochs. In total, ten normal subjects (mean age 38.7 ± 14.6 years) and ten patients with obstructive sleep apnea (OSA) (mean age 44.2 ± 16.5 years) of varying symptom severity participated in this study. Our study detected awakening epochs with an average sensitivity of 85.3% and 85.2%, specificity of 98.4% and 97.7%, accuracy of 97.4% and 96.5%, and Cohen's kappa coefficient of 0.83 and 0.81 for normal subjects and OSA patients, respectively. Also, sleep efficiency was estimated using detected awakening epochs and then compared with PSG results. Mean absolute errors in sleep efficiency were 1.08% and 1.44% for normal subjects and OSA patients, respectively. The results presented here indicate that our suggested method could be reliably applied to real-time nocturnal awakening detection and sleep efficiency estimation. Furthermore, our method may ultimately be an effective tool for long-term, home monitoring of sleep-wake behavior.

Seismocardiogram (SCG) is the measure of the micro-vibrations produced by the heart contraction and blood ejection into the vascular tree. Over time, a large body of evidence has been collected on the ability of SCG to reflect cardiac mechanical events such as opening and closure of mitral and aortic valves, atrial filling and point of maximal aortic blood ejection. We recently developed a smart garment, named MagIC-SCG, that allows the monitoring of SCG, electrocardiogram (ECG) and respiration out of the laboratory setting in ambulant subjects. The present pilot study illustrates the results of two different experiments performed to obtain a first evaluation on whether a dynamical assessment of indexes of cardiac mechanics can be obtained from SCG recordings obtained by MagIC-SCG. In the first experiment, we evaluated the consistency of the estimates of two indexes of cardiac contractility, the pre-ejection period, PEP, and the left ventricular ejection time, LVET. This was done in the lab, by reproducing an experimental protocol well known in literature, so that our measures derived from SCG could have been compared with PEP and LVET reference values obtained by traditional techniques. Six healthy subjects worn MagIC-SCG while assuming two different postures (supine and standing); PEP was estimated as the time interval between the Q wave in ECG and the SCG wave corresponding to the opening of aortic valve; LVET was the time interval between the SCG waves corresponding to the opening and closure of the aortic valve. The shift from supine to standing posture produced a significant increase in PEP and PEP/LVET ratio, a reduction in LVET and a concomitant rise in the LF/HF ratio in the RR interval (RRI) power spectrum. These results are in line with data available in literature thus providing a first support to the validity of our estimates. In the second experiment, we evaluated in one subject the feasibility of the beat-by-beat assessment of LVET during spontaneous behavior. The subject was continuously monitored by the smart garment from 8 am to 8 pm during a workday. From the whole recording, three data segments were selected: while the subject was traveling to work (M1), during work in the office (O) and while traveling back home (M2). LVET was estimated on a beat-by-beat basis from SCG and the RRI influence was removed by regression analysis. The LVET series displayed marked beat-by-beat fluctuations at the respiratory frequency. The amplitude of these fluctuations changed in the three periods and was lower when the LF/HF RRI power ratio was higher, at O, thus suggesting a possible influence of the autonomic nervous system on LVET short-term variability. To the best of our knowledge this case report provides for the first time a representation of the beat-by-beat dynamics of a systolic time interval during daily activity. The statistical characterization of these findings remains to be explored on a larger population., (Copyright © 2013 Elsevier B.V. All rights reserved.)

The transthoracic impedance cardiogram (ICG) and the acceleration ballistocardiogram (BCG) measured close to the center of mass of the human body are generated by changes of blood distribution. The transthoracic ICG is an integrated signal covering the whole thorax and spatial resolution is poor. Comparison between both signals can be used to elucidate the source of the ICG signal. We recorded the ECG, ICG, and BCG simultaneously in healthy subjects under resting and microgravity conditions during parabolic flights. The time interval between the R-peak of the ECG and the maximum of the ICG (R-dZ/dtmax) and the time interval between the R-peak of the ECG and the I-peak in the BCG (R-I) differed significantly (p<0.0001). The I-peak in the BCG always occurred earlier during systole than dZ/dtmax. The delay of dZ/dtmax ranged between 23 and 28 ms at rest and was lowest under microgravity conditions (12 ± 4 ms, p<0.02). Our results suggest that both signals have different sources. Combination of modern imaging techniques with classical non invasive approaches to detect changes of blood distribution may provide new insights into the complex interaction between blood flow and mechanocardiographic signals like the BCG.

Ballistocardiography (BCG) is a promising unobtrusive method for home e-healthcare systems, and has attracted increasing interest in recent years along with technological advances in related biomedical, electrical engineering and computer science fields. While existing systems have investigated the efficacy of BCG setups in bed, backrest, seat or scale positions, we propose to study BCG in headrest position that will allow new practical and portable applications. To this end, we designed and implemented a multi-modality sensing system including a high-sensitivity microbend fiber optic BCG sensor. In this preliminary study, we have collected multi-modality physiological data on 3 human subjects. We ran extensive analysis on BCG in correlation with ECG, and identified special characteristics of the signal in the new BCG setup. The result suggests that new appropriate computing techniques are necessary for accurately recovering the heart beat signal. Therefore, we developed a novel algorithm for heart beat detection. We evaluate the algorithm with the data and demonstrate that it can accurately compute heart rate intervals in the headrest BCG despite significant signal distortion.

The purpose of this work is to study the effect of a tilt table test procedure on ballistocardiographic (BCG) signal by using Electromechanical Film (EMFi) strip sensors. The ECG, BCG, carotid pulse (CP) from the neck near the carotid artery and ankle pulse signals were recorded from 7 persons. The spectral components of the recordings during the tilt table test were studied concentrating mainly on heart induced pulsatile signals propagating along the artery to the periphery. The properties of BCG pulse signal changed due to the tilt test in spectral domain. Blood pressure (BP) values and shape of the pulse changed due to the tilt test. According to this study, local BCG measurements with EMFi sensor strips combined with a tilt test can be used as a very simple non-invasive method in hemodynamic studies.

A Pressure Bed Sensor (PBS) can offer an unobtrusive method for sleep monitoring. This study focuses on the detection of the sleep related breathing disorders using a PBS in comparison to the methods used in a sleep laboratory. A newly developed PCA modeling approach for the eight sensor signals of the PBS is evaluated using the Reduced Respiratory Amplitude Index (RRAI) as a central measure. The method computes the respiration amplitude with the Hilbert transform, and then detects the events based on a 20% amplitude reduction from the baseline signal. A similar calculation was used for the sleep laboratory RIP measurements, and both PBS and RIP were compared against the reference based on the nasal flow signal. In the reference RRAI method, the respiratory-disordered events were obtained using RemLogic respiration analyzer to detect over 50% amplitude reduction in the nasal respiratory flow, but removing the RemLogic standard hypopnea event associations on the oxygen desaturation events and the sleep arousals. The movement artifacts were automatically detected based on the movement activity signal of the PBS. Twenty-five (25) out of 28 patients were finally analysed. On average 87% of a night measurement has been covered by the system. The correlation coefficient was 0.92 between the PBS and the reference RRAI, and the performance of the PBS was similar with the RIP belts. Classifying the severity of the sleep related breathing by dividing RRAI in groups according to the severity criteria, the sensitivity was 92% and the specificity was 70% for the PBS. The results suggest that PBS recording can provide an easy and un-obstructive alternative method for the detection of the sleep disordered breathing and thus has a great promise for the home monitoring.

Ballistocardiography (BCG) is a non-invasive technology that has been used to record ultra-low-frequency vibrations of the heart allowing for the measurement of cardiac cycle events including timing and amplitudes of contraction. Recent developments in BCG have made this technology simple to use, as well as time- and cost-efficient in comparison with other more complicated and invasive techniques used to evaluate cardiac performance. Recent technological advances are considerably greater since the advent of microprocessors and laptop computers. Along with the history of BCG, this paper reviews the present and future potential benefits of using BCG to measure cardiac cycle events and its application to clinical and applied research., (© 2012 The Authors Clinical Physiology and Functional Imaging © 2012 Scandinavian Society of Clinical Physiology and Nuclear Medicine.)

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.

Ballistocardiography (BCG) is a non-invasive method for monitoring cardiovascular health. While in the early 1900s, when it was invented, the BCG was intended to be a tool used exclusively in the clinic, the recent resurgence of BCG research has actually focused on extra-clinical applications ranging from home monitoring to measuring signals from astronauts in space. This repositioning of the diagnostic technique has largely been spurred by recent advances in measurement technology: historically, BCG instrumentation was large, cumbersome, and difficult to maintain; currently, it is small, easy-to-use, and does not require any sophisticated maintenance. This review presents the latest technological improvements in BCG instrumentation. These developments should further help to establish the BCG as a useful diagnostic tool in the coming years.

Encouraged by previous performance of a hydraulic bed sensor, this work presents a new hydraulic transducer configuration which improves the system's ability to capture a heartbeat signal from four subjects with different body weight and height, gender, age and cardiac history. It also proposes a new approach for detecting the occurrence of heartbeats from ballistocardiogram (BCG) signals through the use of the k-means clustering algorithm, based on finding the location of the J-peaks. Preliminary testing showed that the new transducer arrangement was able to capture the occurrence of heartbeats for all the participants, and the clustering approach achieved correct heartbeat detection ranging from 98.6 to 100% for three of them. Some considerations are discussed regarding adjustments that can be done in order to increase the correct detection of heartbeats for the participant whose percentage of correct detection ranged from 71.0 to 92.5%.

Traditionally, ballistocardiogram (BCG) has been measured using large and stationary devices. In this work, we demonstrate a portable and continuous BCG monitor that is wearable at the ear. The device has the form factor of a hearing aid and is wirelessly connected to a PC for data recording and analysis. With the ear as an anchoring point, the device uses a MEMS tri-axial accelerometer to measure BCG at the head. Morphological differences exist between head BCG and traditional BCG, but the principal peaks (J waves) and their vectors are preserved. The frequency of J waves corresponds to heart rate, and when used in conjunction with an electrocardiogram's (ECG) R wave, the timing of J waves yields the RJ interval. Results from our clinical study show linear correlation between the RJ interval and the heart's pre-ejection period during hemodynamic maneuvers, thus revealing important information about cardiac contractility and its regulation.

We developed nonintrusive methods for simultaneous electrocardiogram, photoplethysmogram, and ballistocardiogram measurements that do not require direct contact between instruments and bare skin. These methods were applied to the design of a diagnostic chair for unconstrained heart rate and blood pressure monitoring purposes. Our methods were operationalized through capacitively coupled electrodes installed in the chair back that include high-input impedance amplifiers, and conductive textiles installed in the seat for capacitive driven-right-leg circuit configuration that is capable of recording electrocardiogram information through clothing. Photoplethysmograms were measured through clothing using seat mounted sensors with specially designed amplifier circuits that vary in light intensity according to clothing type. Ballistocardiograms were recorded using a film type transducer material, polyvinylidenefluoride (PVDF), which was installed beneath the seat cover. By simultaneously measuring signals, beat-to-beat heart rates could be monitored even when electrocardiograms were not recorded due to movement artifacts. Beat-to-beat blood pressure was also monitored using unconstrained measurements of pulse arrival time and other physiological parameters, and our experimental results indicated that the estimated blood pressure tended to coincide with actual blood pressure measurements. This study demonstrates the feasibility of our method and device for biological signal monitoring through clothing for unconstrained long-term daily health monitoring that does not require user awareness and is not limited by physical activity.

Sternal seismocardiogram (SCG) is the assessment of microvibrations produced by the beating heart as detected by an accelerometer positioned on the sternum. This signal reflects mechanical events of the heart contraction, including the opening and closure of mitral and aortic valves and maximal blood flow acceleration. Traditionally, SCG has been detected in a laboratory setting with the subject lying at rest in supine position. Aims of this study were 1) to investigate the feasibility of a SCG monitoring over the 24 hours in ambulant subjects, and 2) to calculate number and time distribution of the SCG estimates obtainable over the 24 hours. In 5 healthy subjects ECG, respiration, body accelerations and sternal SCG were recorded for 24 hours in a workday by a smart garment recently developed in our laboratory, the MagIC-SCG system. Each recording was split into a series of contiguous 5-s data segments and SCG was estimated in each segment where the magnitude of the acceleration vector was < 4 milli-g (this condition indicates that the subject was not moving).All the 24-h recordings were found of good quality and could be entirely analyzed. A large number of SCG estimates could be obtained over the 24 hours. In particular, more than 100 estimates per hour were available during the day; at night this rate was three times higher.Thus our study indicates that not only the 24h SCG monitoring in daily life is feasible but also that possible changes over time in SCG and its derived parameters may be tracked with an extreme temporal detail.

This paper describes the circuits and signal processing techniques that convert an electronic bathroom scale intended for bioimpedance analysis (BIA) into a compact system to acquire the electrocardiogram (ECG), the ballistocardiogram (BCG), and the impedance plethysmogram (IPG) using only plantar measurements. The signal processing methods proposed rely on the higher quality of the IPG as compared to the ECG and BCG and they enhance the signal-to-noise ratio (SNR) of these two signals, which otherwise could be too poor in non-controlled environments. The system is suitable for long-term periodic monitoring of cardiovascular function.

Using the Ballistocardiogram(BCG) measured on weighing scale, heart rate variability(HRV) and blood pressure were estimated. BCG was measured while subjects were on weighing scale in resting state and under the Valsalva maneuver and static exercise condition to induce the change in cardiac autonomic rhythm. Time domain, frequency domain and nonlinear HRV parameters were estimated from the measured BCG and compared with the ones calculated from ECG measured simultaneously. For blood pressure(BP) estimation, ECG was measured additionally on the feet using dry electrodes simultaneously installed on weighing scale and R-J intervals were extracted as a BP correlated parameter at every beat cycle. HRV estimation results shows the correlation higher than 0.97, and the estimated BP was similar to the measured BP with a reliable correlations.

Our work covers improvements in sensors and signal processing for unobtrusive, long-term monitoring of cardiac (and respiratory) rhythms using only non-invasive vibration sensors. We describe a system for the unobtrusive monitoring of vital signs by means of an array of novel optical ballistocardiography (BCG) sensors placed underneath a regular bed mattress. Furthermore, we analyze the systems spatial sensitivity and present proof-of-concept results comparing our system to a more conventional BCG system based on a single electromechanical-film (EMFi) sensor. Our preliminary results suggest that the proposed optical multi-channel system could have the potential to reduce beat-to-beat heart rate estimation errors, as well as enable the analysis of more complex breathing patterns.

Short and long-term alterations of ballistocardiographic parameters (BCG) were studied in a sitting position using Electromechanical Film (EMFi) sensors. Several physiological parameters were recorded from 48 men of middle age in consecutive recordings and after a 2-week interval. The duration and amplitudes of the signal components were studied. The repeatability index R, the Pearson correlation, and Bland-Altman (BA) repeatability methods were used to determine how repeatable the studied signals are. In consecutive and during 2 week recordings, the Pearson correlation rates for BCG systolic component values (from 0.88/0.83 (T(RJ)) to 0.97/0.78 (A(IJ)), PWV values for the left 0.97/0.67 and right ankle 0.98/0.76) were detected indicating excellent repeatability during consecutive recordings. Respectively BA T(RJ) was -0.002 ± 0.028/-0.004 ± 0.030. Corresponding R values were T(RJ) 0.90/0.82 and right ankle PWV 0.98/0.81. Quite modest blood pressure R values were obtained between 2 week recordings (systolic 0.63, diastolic 0.75, and heart rate 0.59). BCG and PWV measured with EMFi sensors are highly repeatable in consecutive recordings. In longer time interval recordings, BCG is influenced by hemodynamic instability, but in spite of this the reproducibility of the main parameters of the BCG and PWV were better than measured heart rate and blood pressure values.

Ubiquitous medical technology may provide advanced utility for evaluating the status of the patient beyond the clinical environment. The iPhone provides the capacity to measure the heart rate, as the iPhone consists of a 3-axis accelerometer that is sufficiently sensitive to perceive tiny body movements caused by heart pumping. In this preliminary study, an iPhone was tested and evaluated as the reliable heart rate extractor to use for medical purpose by comparing with reference electrocardiogram. By comparing the extracted heart rate from acquired acceleration data with the extracted one from ECG reference signal, iPhone functioning as the reliable heart rate extractor has demonstrated sufficient accuracy and consistency.

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.

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.

To evaluate sleep quality or autonomic nervous system, many annoying electrodes have be attached to subjects' body. It can disturb comfortable sleep and, moreover, since it is very expensive experiment, continuous sleep monitoring is difficult. Since heart rate reflects the autonomic nervous system, it is highly synchronized with the sympathetic activation during transition from non-REM sleep to wakefulness. When the transition occurred the heart rate abruptly increased clearly distinguished with other changes. By using this physiology, we tried to classify the wakefulness during the whole night sleep. Our final goal is adopting this method to the continuous monitoring in our daily life. electrocardiogram (ECG) is not the suitable. Subjects have to attach the electrodes by themselves in their housing to obtain ECG. In that point of view, we used the ballistocardiogram (BCG) that is the representative method to obtain heart beat nonintrusively. For ten normal subjects, the wakefulness classifications by using the heart rate dynamics were executed. Nine subjects showed substantial agreement with the visually-scored method, polysomnography (PSG), and only one subject showed moderate agreement in Cohen's kappa value.

The purpose of this research is to demonstrate and compare the utilization of electromechanical film (EMFi) and two acceleration sensors, ADXL202 and MXA2500U, for ballistocardiographic (BCG) and pulse transit time (PTT) studies. We have constructed a mobile physiological measurement station including amplifiers and a data collection system to record the previously mentioned signals and an electrocardiogram signal. Various versions of the measuring systems used in BCG studies in the past are also presented and evaluated. We have showed the ability of the EMFi sensor to define the elastic properties of the cardiovascular system and to ensure the functionality of the proposed instrumentation in different physiological loading conditions, before and after exercise and sauna bath. The EMFi sensor provided a BCG signal of good quality in the study of the human heart and function of the cardiovascular system with different measurement configurations. EMFi BCG measurements provided accurate and repeatable results for the different components of the heart cycle. In multiple-channel EMFi measurements, the carotid and limb pulse signals acquired were detailed and distinctive, allowing accurate PTT measurements. Changes in blood pressure were clearly observed and easily determined with EMFi sensor strips in pulse wave velocity (PWV) measurements. In conclusion, the configuration of the constructed device provided reliable measurements of the electrocardiogram, BCG, heart sound, and carotid and ankle pulse wave signals. Attached EMFi sensor strips on the neck and limbs yield completely new applications of the EMFi sensors aside from the conventional seat and supine recordings. Higher sensitivity, ease of utilization, and minimum discomfort of the EMFi sensor compared with acceleration sensors strengthen the status of the EMFi sensor for accurate and reliable BCG and PWV measurements, providing novel evaluation of the elastic properties of the cardiovascular system.

Cardiac ejection of blood into the aorta generates a reaction force on the body that can be measured externally via the ballistocardiogram (BCG). In this study, a commercial bathroom scale was modified to measure the BCGs of nine healthy subjects recovering from treadmill exercise. During the recovery, Doppler echocardiogram signals were obtained simultaneously from the left ventricular outflow tract of the heart. The percentage changes in root-mean-square (RMS) power of the BCG were strongly correlated with the percentage changes in cardiac output measured by Doppler echocardiography (R(2) = 0.85, n = 275 data points). The correlation coefficients for individually analyzed data ranged from 0.79 to 0.96. Using Bland-Altman methods for assessing agreement, the mean bias was found to be -0.5% (+/-24%) in estimating the percentage changes in cardiac output. In contrast to other non-invasive methods for trending cardiac output, the unobtrusive procedure presented here uses inexpensive equipment and could be performed without the aid of a medical professional.

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.