Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive Monitoring of Vital Signs

Owing to accelerated societal aging, the prevalence of elderly individuals experiencing solitary or sudden death at home has increased. Therefore, herein, we aimed to develop a monitoring system that utilizes piezoelectric sensors for the non-invasive and non-restrictive monitoring of vital signs, including the heart rate and respiration, to detect changes in the health status of several elderly individuals. A ballistocardiogram with a piezoelectric sensor was tested using seven individuals. The frequency spectra of the biosignals acquired from the piezoelectric sensors exhibited multiple peaks corresponding to the harmonics originating from the heartbeat. We aimed for individual identification based on the shapes of these peaks as the recognition criteria. The results of individual identification using deep learning techniques revealed good identification proficiency. Altogether, the monitoring system integrated with piezoelectric sensors showed good potential as a personal identification system for identifying individuals with abnormal biological signals.

Non-contact assessment of cardiac physiology using FO-MVSS-based ballistocardiography: a promising approach for heart failure evaluation

Continuous monitoring of cardiac motions has been expected to provide essential cardiac physiology information on cardiovascular functioning. A fiber-optic micro-vibration sensing system (FO-MVSS) makes it promising. This study aimed to explore the correlation between Ballistocardiography (BCG) waveforms, measured using an FO-MVSS, and myocardial valve activity during the systolic and diastolic phases of the cardiac cycle in participants with normal cardiac function and patients with congestive heart failure (CHF). A high-sensitivity FO-MVSS acquired continuous BCG recordings. The simultaneous recordings of BCG and electrocardiogram (ECG) signals were obtained from 101 participants to examine their correlation. BCG, ECG, and intracavitary pressure signals were collected from 6 patients undergoing cardiac catheter intervention to investigate BCG waveforms and cardiac cycle phases. Tissue Doppler imaging (TDI) measured cardiac time intervals in 51 participants correlated with BCG intervals. The BCG recordings were further validated in 61 CHF patients to assess cardiac parameters by BCG. For heart failure evaluation machine learning was used to analyze BCG-derived cardiac parameters. Significant correlations were observed between cardiac physiology parameters and BCG's parameters. Furthermore, a linear relationship was found betwen IJ amplitude and cardiac output (r = 0.923, R2 = 0.926, p < 0.001). Machine learning techniques, including K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and XGBoost, respectively, demonstrated remarkable performance. They all achieved average accuracy and AUC values exceeding 95% in a five-fold cross-validation approach. We establish an electromagnetic-interference-free and non-contact method for continuous monitoring of the cardiac cycle and myocardial contractility and measure the different phases of the cardiac cycle. It presents a sensitive method for evaluating changes in both cardiac contraction and relaxation in the context of heart failure assessment.

BCG Signal Quality Assessment Based on Time-Series Imaging Methods

This paper describes a signal quality classification method for arm ballistocardiogram (BCG), which has the potential for non-invasive and continuous blood pressure measurement. An advantage of the BCG signal for wearable devices is that it can easily be measured using accelerometers. However, the BCG signal is also susceptible to noise caused by motion artifacts. This distortion leads to errors in blood pressure estimation, thereby lowering the performance of blood pressure measurement based on BCG. In this study, to prevent such performance degradation, a binary classification model was created to distinguish between high-quality versus low-quality BCG signals. To estimate the most accurate model, four time-series imaging methods (recurrence plot, the Gramain angular summation field, the Gramain angular difference field, and the Markov transition field) were studied to convert the temporal BCG signal associated with each heartbeat into a 448 × 448 pixel image, and the image was classified using CNN models such as ResNet, SqueezeNet, DenseNet, and LeNet. A total of 9626 BCG beats were used for training, validation, and testing. The experimental results showed that the ResNet and SqueezeNet models with the Gramain angular difference field method achieved a binary classification accuracy of up to 87.5%.

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review

Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body's center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time.

Validation of Dozee, a Ballistocardiography-based Device, for Contactless and Continuous Heart Rate and Respiratory Rate Measurement

Long-term acquisition of respiratory and heart signals is useful in a variety of applications, including sleep analysis, monitoring of respiratory and heart disorders, and so on. Ballistocardiography (BCG), a non-invasive technique that measures micro-body vibrations caused by cardiac contractions as well as motion caused by breathing, snoring, and body movements, would be ideal for long-term vital parameter acquisition. Turtle Shell Technologies Pvt. Ltd.'s Dozee device, which is based on BCG, is a contactless continuous vital parameters monitoring system. It is designed to measure Heart Rate (HR) and Respiratory Rate (RR) continuously and without contact in a hospital setting or at home. A validation study for HR and RR was conducted using Dozee by comparing it to the vitals obtained from the FDA-approved Patient Monitor. This was done in a sleep laboratory setting over 110 nights in 51 subjects to evaluate HR and over 20 nights in 17 subjects to evaluate RR at the National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India. Approximately 789 hours data for HR and approximately 112 hours data for RR was collected. Dozee was able to achieve a mean absolute error of 1.72 bpm for HR compared to the gold standard ECG. A mean absolute error of ∼1.24 breaths/min was obtained in determining RR compared to currently used methods. Dozee is ideal for long-term contactless monitoring of vital parameters due to its low mean absolute errors in measuring both HR and RR. Clinical Relevance- Continuous and long-term vitals monitoring is known to enable early screening of clinical deterioration, improve patient outcomes and reduce mortality. Current methods of continuous monitoring are overly complex, costly, and rely heavily on patient compliance. The proposed remote vitals monitoring solution based on BCG was found to be at par with gold standard methods of recording HR and RR. As a result, clinicians can use it to effectively monitor patients in both the hospital and at home.

Beat to BEAT - Non-Invasive Investigation of Cardiac Function on the International Space Station

This paper describes the protocol of the microgravity experiment BEAT (Ballistocardiography for Extraterrestrial Applications and Long-Term Missions). The current study makes use of signal acquisition of cardiac parameters with a high-precision Ballistocardiography (BCG)/Seismocardiography (SCG) measurement system, which is integrated in a smart shirt (SmartTex). The goal is to evaluate the feasibility of this concept for continuous wearable monitoring and wireless data transfer. BEAT is part of the "Wireless Compose-2" (WICO2) project deployed on the International Space Station (ISS) that will provide wireless network infrastructure for scientific, localization and medical experiments.

A Comparison of Alternative Approaches to MR Cardiac Triggering: A Pilot Study at 3 Tesla

This pilot comparative study evaluates the usability of the alternative approaches to magnetic resonance (MR) cardiac triggering based on ballistocardiography (BCG): fiber-optic sensor (O-BCG) and pneumatic sensor (P-BCG). The comparison includes both the objective and subjective assessment of the proposed sensors in comparison with a gold standard of ECG-based triggering. The objective evaluation included several image quality assessment (IQA) parameters, whereas the subjective analysis was performed by 10 experts rating the diagnostic quality (scale 1 - 3, 1 corresponding to the best image quality and 3 the worst one). Moreover, for each examination, we provided the examination time and comfort rating (scale 1 - 3). The study was performed on 10 healthy subjects. All data were acquired on a 3 T SIEMENS MAGNETOM Prisma. In image quality analysis, all approaches reached comparable results, with ECG slightly outperforming the BCG-based methods, especially according to the objective metrics. The subjective evaluation proved the best quality of ECG (average score of 1.68) and higher performance of P-BCG (1.97) than O-BCG (2.03). In terms of the comfort rating and total examination time, the ECG method achieved the worst results, i.e. the highest score and the longest examination time: 2.6 and 10:49 s, respectively. The BCG-based alternatives achieved comparable results (P-BCG 1.5 and 8:06 s; OBCG 1.9, 9:08 s). This study confirmed that the proposed BCG-based alternative approaches to MR cardiac triggering offer comparable quality of resulting images with the benefits of reduced examination time and increased patient comfort.

Digital Optical Ballistocardiographic System for Activity, Heart Rate, and Breath Rate Determination during Sleep

In this work, we present a ballistocardiographic (BCG) system for the determination of heart and breath rates and activity of a user lying in bed. Our primary goal was to simplify the analog and digital processing usually required in these kinds of systems while retaining high performance. A novel sensing approach is proposed consisting of a white LED facing a digital light detector. This detector provides precise measurements of the variations of the light intensity of the incident light due to the vibrations of the bed produced by the subject's breathing, heartbeat, or activity. Four small springs, acting as a bandpass filter, connect the boards where the LED and the detector are mounted. Owing to the mechanical bandpass filtering caused by the compressed springs, the proposed system generates a BCG signal that reflects the main frequencies of the heartbeat, breathing, and movement of the lying subject. Without requiring any analog signal processing, this device continuously transmits the measurements to a microcontroller through a two-wire communication protocol, where they are processed to provide an estimation of the parameters of interest in configurable time intervals. The final information of interest is wirelessly sent to the user's smartphone by means of a Bluetooth connection. For evaluation purposes, the proposed system has been compared with typical BCG systems showing excellent performance for different subject positions. Moreover, applied postprocessing methods have shown good behavior for information separation from a single-channel signal. Therefore, the determination of the heart rate, breathing rate, and activity of the patient is achieved through a highly simplified signal processing without any need for analog signal conditioning.

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

Benefiting from non-invasive sensing technologies, 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, 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 24 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.

ResNet-BiLSTM: A Multiscale Deep Learning Model for Heartbeat Detection Using Ballistocardiogram Signals

As the heartbeat detection from ballistocardiogram (BCG) signals using force sensors is interfered by respiratory effort and artifact motion, advanced signal processing algorithms are required to detect the J-peak of each BCG signal so that beat-to-beat interval can be identified. However, existing methods generally rely on rule-based detection of a fixed size, without considering the rhythm features in a large time scale covering multiple BCG signals. Methods. This paper develops a deep learning framework based on ResNet and bidirectional long short-term memory (BiLSTM) to conduct beat-to-beat detection of BCG signals. Unlike the existing methods, the proposed network takes multiscale features of BCG signals as the input and, thus, can enjoy the complementary advantages of both morphological features of one BCG signal and rhythm features of multiple BCG signals. Different time scales of multiscale features for the proposed model are validated and analyzed through experiments. Results. The BCG signals recorded from 21 healthy subjects are conducted to verify the performance of the proposed heartbeat detection scheme using leave-one-out cross-validation. The impact of different time scales on the detection performance and the performance of the proposed model for different sleep postures are examined. Numerical results demonstrate that the proposed multiscale model performs robust to sleep postures and achieves an averaged absolute error (E abs) and an averaged relative error (E rel) of the heartbeat interval relative to the R-R interval of 9.92 ms and 2.67 ms, respectively, which are superior to those of the state-of-the-art detection protocol. Conclusion. In this work, a multiscale deep-learning model for heartbeat detection using BCG signals is designed. We demonstrate through the experiment that the detection with multiscale features of BCG signals can provide a superior performance to the existing works. Further study will examine the ultimate performance of the multiscale model in practical scenarios, i.e., detection for patients suffering from cardiovascular disorders with night-sleep monitoring.

Assessment of left ventricular twist by 3D ballistocardiography and seismocardiography compared with 2D STI echocardiography in a context of enhanced inotropism in healthy subjects

Ballistocardiography (BCG) and Seismocardiography (SCG) assess the vibrations produced by cardiac contraction and blood flow, respectively, by means of micro-accelerometers and micro-gyroscopes. From the BCG and SCG signals, maximal velocities (VMax), integral of kinetic energy (iK), and maximal power (PMax) can be computed as scalar parameters, both in linear and rotational dimensions. Standard echocardiography and 2-dimensional speckle tracking imaging echocardiography were performed on 34 healthy volunteers who were infused with increasing doses of dobutamine (5-10-20 μg/kg/min). Linear VMax of BCG predicts the rates of left ventricular (LV) twisting and untwisting (both p < 0.0001). The linear PMax of both SCG and BCG and the linear iK of BCG are the best predictors of the LV ejection fraction (LVEF) (p < 0.0001). This result is further confirmed by mathematical models combining the metrics from SCG and BCG signals with heart rate, in which both linear PMax and iK strongly correlate with LVEF (R = 0.7, p < 0.0001). In this setting of enhanced inotropism, the linear VMax of BCG, rather than the VMax of SCG, is the metric which best explains the LV twist mechanics, in particular the rates of twisting and untwisting. PMax and iK metrics are strongly associated with the LVEF and account for 50% of the variance of the LVEF.

Single-layered ultra-soft washable smart textiles for all-around ballistocardiograph, respiration, and posture monitoring during sleep

Good sleep is considered to be the cornerstone for maintaining both physical and mental health. However, nearly one billion people worldwide suffer from various sleep disorders. To date, polysomnography (PSG) is the most commonly used sleep-monitoring technology,however, it is complex, intrusive, expensive and uncomfortable. Unfortunately, present noninvasive monitoring technologies cannot simultaneously achieve high sensitivity, multi-parameter monitoring and comfort. Here, we present a single-layered, ultra-soft, smart textile for all-around physiological parameters monitoring and healthcare during sleep. With a high-pressure sensitivity of 10.79 mV/Pa, a wide working frequency bandwidth from 0 Hz to 40 Hz, good stability, and decent washability, the single-layered ultra-soft smart textile is simultaneously capable of real-time detection and tracking of dynamic changes in sleep posture, and subtle respiration and ballistocardiograph (BCG) monitoring. Using the set of patient generated health data, an obstructive sleep apnea-hypopnea syndrome (OSAHS) monitoring and intervention system was also developed to improve the sleep quality and prevent sudden death during sleep. This work is expected to pave a new and practical pathway for physiological monitoring during sleep.

Ballistocardiography Can Estimate Beat-to-Beat Heart Rate Accurately at Night in Patients After Vascular Intervention

While bed-integrated ballistocardiography (BCG) has potential clinical applications such as unobtrusive monitoring of patients staying in the general hospital ward, it has so far mainly gained interest in the wellness domain. In this article, the potential of BCG to monitor hospitalized patients after surgical intervention was assessed. Long-term BCG recordings (mean duration 17.7 h) of 14 patients were performed with an EMFit QS bed sensor. In addition, ten healthy subjects were recorded during sleep (mean duration 7.8 h). Using an iterative algorithm, beat-to-beat intervals (BBIs) and the ultra-short-term heart-rate-variability (HRV) parameters standard deviation of NN intervals (SDNN) and root mean square of successive differences (RMSSD) were estimated and compared to an ECG reference in terms of average estimation error and temporal coverage. While the absolute BBI estimation error was found to be higher when full-day patient data was used (16.5 ms), no significant difference between healthy subjects (12.7 ms) and patient nighttime data (11.0 ms) was observed. Nevertheless, temporal coverage of BBI estimation was significantly lower in patients (39.3% overall, 51.7% at night) compared to the healthy sleepers (73.2%). This resulted in reduced HRV estimation coverage (9.7% vs. 37.2%) at comparable estimation error levels.

Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection

Body acceleration due to heartbeat-induced reaction forces can be measured as mobile phone accelerometer (m-ACC) signals. Our aim was to test the feasibility of using m-ACC to detect changes induced by stress by ultra-short heart rate variability (USV) indices (standard deviation of normal-to-normal interval—SDNN and root mean square of successive differences—RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing at rest conditions (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional electrocardiogram (ECG). Beat occurrences were extracted from both ECG and m-ACC and used to compute USV indices using 60, 30 and 10 s durations, both for REST and MS. A feasibility of 93.8% in the beat-to-beat m-ACC heart rate series extraction was reached. In both ECG and m-ACC series, compared to REST, in MS the mean beat duration was reduced by 15% and RMSSD decreased by 38%. These results show that short term recordings (up to 10 s) of cardiac activity using smartphone’s accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus.

A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning

The ballistocardiography (BCG) signal is a measurement of the vibrations of the center of mass of the body due to the cardiac cycle and can be used for noninvasive hemodynamic monitoring. The seismocardiography (SCG) signals measure the local vibrations of the chest wall due to the cardiac cycle. While BCG is a more well-known modality, it requires the use of a modified bathroom scale or a force plate and cannot be measured in a wearable setting, whereas SCG signals can be measured using wearable accelerometers placed on the sternum. In this paper, we explore the idea of finding a mapping between zero mean and unit l₂-norm SCG and BCG signal segments such that, the BCG signal can be acquired using wearable accelerometers (without retaining amplitude information). We use neural networks to find such a mapping and make use of the recently introduced UNet architecture. We trained our models on 26 healthy subjects and tested them on ten subjects. Our results show that we can estimate the aforementioned segments of the BCG signal with a median Pearson correlation coefficient of 0.71 and a median absolute deviation (MAD) of 0.17. Furthermore, our model can estimate the R-I, R-J and R-K timing intervals with median absolute errors (and MAD) of 10.00 (8.90), 6.00 (5.93), and 8.00 (5.93), respectively. We show that using all three axis of the SCG accelerometer produces the best results, whereas the head-to-foot SCG signal produces the best results when a single SCG axis is used.

Mitigation of Instrument-Dependent Variability in Ballistocardiogram Morphology: Case Study on Force Plate and Customized Weighing Scale

The objective of this study was to investigate the measurement instrument-dependent variability in the morphology of the ballistocardiogram (BCG) waveform in human subjects and computational methods to mitigate the variability. The BCG was measured in 22 young healthy subjects using a high-performance force plate and a customized commercial weighing scale under upright standing posture. The timing and amplitude features associated with the major I, J, K waves in the BCG waveforms were extracted and quantitatively analyzed. The results indicated that 1) the I, J, K waves associated with the weighing scale BCG exhibited delay in the timings within the cardiac cycle relative to the ECG R wave as well as attenuation in the absolute amplitudes than the respective force plate counterparts, whereas 2) the time intervals between the I, J, K waves were comparable. Then, two alternative computational methods were conceived in an attempt to mitigate the discrepancy between force plate versus weighing-scale BCG: a transfer function and an amplitude-phase correction. The results suggested that both methods effectively mitigated the discrepancy in the timings and amplitudes associated with the I, J, K waves between the force plate and weighing-scale BCG. Hence, signal processing may serve as a viable solution to the mitigation of the instrument-induced morphological variability in the BCG, thereby facilitating the standardized analysis and interpretation of the timing and amplitude features in the BCG across wide-ranging measurement platforms.

Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling

OBJECTIVE

To develop quantitative methods for the clinical interpretation of the ballistocardiogram (BCG).

METHODS

A closed-loop mathematical model of the cardiovascular system is proposed to theoretically simulate the mechanisms generating the BCG signal, which is then compared with the signal acquired via accelerometry on a suspended bed.

RESULTS

Simulated arterial pressure waveforms and ventricular functions are in good qualitative and quantitative agreement with those reported in the clinical literature. Simulated BCG signals exhibit the typical I, J, K, L, M, and N peaks and show good qualitative and quantitative agreement with experimental measurements. Simulated BCG signals associated with reduced contractility and increased stiffness of the left ventricle exhibit different changes that are characteristic of the specific pathological condition.

CONCLUSION

The proposed closed-loop model captures the predominant features of BCG signals and can predict pathological changes on the basis of fundamental mechanisms in cardiovascular physiology.

SIGNIFICANCE

This paper provides a quantitative framework for the clinical interpretation of BCG signals and the optimization of BCG sensing devices. The present paper considers an average human body and can potentially be extended to include variability among individuals.

Vital Sign Monitoring and Cardiac Triggering at 1.5 Tesla: A Practical Solution by an MR-Ballistocardiography Fiber-Optic Sensor

This article presents a solution for continuous monitoring of both respiratory rate (RR) and heart rate (HR) inside Magnetic Resonance Imaging (MRI) environments by a novel ballistocardiography (BCG) fiber-optic sensor. We designed and created a sensor based on the Fiber Bragg Grating (FBG) probe encapsulated inside fiberglass (fiberglass is a composite material made up of glass fiber, fabric, and cured synthetic resin). Due to this, the encapsulation sensor is characterized by very small dimensions (30 × 10 × 0.8 mm) and low weight (2 g). We present original results of real MRI measurements (conventionally most used 1.5 T MR scanner) involving ten volunteers (six men and four women) by performing conventional electrocardiography (ECG) to measure the HR and using a Pneumatic Respiratory Transducer (PRT) for RR monitoring. The acquired sensor data were compared against real measurements using the objective Bland–Altman method, and the functionality of the sensor was validated (95.36% of the sensed values were within the ±1.96 SD range for the RR determination and 95.13% of the values were within the ±1.96 SD range for the HR determination) by this means. The accuracy of this sensor was further characterized by a relative error below 5% (4.64% for RR and 4.87% for HR measurements). The tests carried out in an MRI environment demonstrated that the presence of the FBG sensor in the MRI scanner does not affect the quality of this imaging modality. The results also confirmed the possibility of using the sensor for cardiac triggering at 1.5 T (for synchronization and gating of cardiovascular magnetic resonance) and for cardiac triggering when a Diffusion Weighted Imaging (DWI) is used.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

The impact of additive noise on BCG morphology and estimated physiological parameters can be analyzed using the developed methodology without requiring prior statistics.

SIGNIFICANCE

This paper can facilitate the performance evaluation of BCG analysis algorithms against additive noise.

Monitoring the Relative Blood Pressure Using a Hydraulic Bed Sensor System

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.

Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring From Ballistocardiograms

A multiple instance dictionary learning approach, dictionary learning using functions of multiple instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a "heartbeat concept" that represents an individual's personal ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection and heartbeat characterization as a multiple instance learning problem to address the uncertainty inherent in aligning BCG signals with ground truth during training. Experimental results show that the estimated heartbeat concept obtained by DL-FUMI is an effective heartbeat prototype and achieves superior performance over comparison algorithms.

Heart-Brain Interactions in the MR Environment: Characterization of the Ballistocardiogram in EEG Signals Collected During Simultaneous fMRI

The ballistocardiographic (BCG) artifact is linked to cardiac activity and occurs in electroencephalographic (EEG) recordings acquired inside the magnetic resonance (MR) environment. Its variability in terms of amplitude, waveform shape and spatial distribution over subject's scalp makes its attenuation a challenging task. In this study, we aimed to provide a detailed characterization of the BCG properties, including its temporal dependency on cardiac events and its spatio-temporal dynamics. To this end, we used high-density EEG data acquired during simultaneous functional MR imaging in six healthy volunteers. First, we investigated the relationship between cardiac activity and BCG occurrences in the EEG recordings. We observed large variability in the delay between ECG and subsequent BCG events (ECG-BCG delay) across subjects and non-negligible epoch-by-epoch variations at the single subject level. The inspection of spatial-temporal variations revealed a prominent non-stationarity of the BCG signal. We identified five main BCG waves, which were common across subjects. Principal component analysis revealed two spatially distinct patterns to explain most of the variance (85% in total). These components are possibly related to head rotation and pulse-driven scalp expansion, respectively. Our results may inspire the development of novel, more effective methods for the removal of the BCG, capable of isolating and attenuating artifact occurrences while preserving true neuronal activity.

Ballistocardiogram-Based Approach to Cuffless Blood Pressure Monitoring: Proof of Concept and Potential Challenges

OBJECTIVE

The goal was to propose and establish the proof of concept of an ultraconvenient cuffless blood pressure monitoring approach based on the ballistocardiogram.

METHODS

The proposed approach monitors blood pressure by exploiting two features in the whole-body head-to-foot ballistocardiogram measured using a force plate: the time interval between the first ("I") and second ("J") major waves ("I-J interval") for diastolic pressure and the amplitude between the J and third major ("K") waves ("J-K amplitude") for pulse pressure. The efficacy of the approach was examined in 22 young healthy volunteers by investigating the diastolic pressure monitoring performance of pulse transit time, pulse arrival time, and ballistocardiogram's I-J interval, and the systolic pressure monitoring performance of pulse transit time and I-J interval in conjunction with ballistocardiogram's J-K amplitude.

RESULTS

The I-J interval was comparable to pulse transit time and pulse arrival time in monitoring diastolic pressure, and the J-K amplitude could provide meaningful improvement to pulse transit time and I-J interval in monitoring systolic pressure.

CONCLUSION

The ballistocardiogram may contribute toward ultraconvenient and more accurate cuffless blood pressure monitoring.

SIGNIFICANCE

The proposed approach has potential to complement the pulse transit time technique for cuffless blood pressure monitoring in two ways. First, it may be integrated with pulse transit time to enable independent monitoring of diastolic and systolic pressures via the J-K amplitude. Second, it may even enable diastolic and systolic pressure monitoring from the ballistocardiogram alone.

Non-invasive BCG monitoring for non-traditional settings

This paper presents the results from actual measurements of cardiac activity acquired through the use of noninvasive sensors to detect Ballistocardiogram (BCG). The results show that it is feasible to unobtrusively monitor heart rate in non-standard settings such as waiting rooms or at school using simple chairs fitted with capacitive sensors. The selected sensors, based on electromechanical principles, are able to measure BCG from a variety of subjects. We present the results for 114 participants from homes, school and a hospital waiting room, adding up over 815 minutes of data.

Simultaneous Monitoring of Ballistocardiogram and Photoplethysmogram Using a Camera

We present a noncontact method to measure ballistocardiogram (BCG) and photoplethysmogram (PPG) simultaneously using a single camera. The method tracks the motion of facial features to determine displacement BCG, and extracts the corresponding velocity and acceleration BCGs by taking first and second temporal derivatives from the displacement BCG, respectively. The measured BCG waveforms are consistent with those reported in the literature and also with those recorded with an accelerometer-based reference method. The method also tracks PPG based on the reflected light from the same facial region, which makes it possible to track both BCG and PPG with the same optics. We verify the robustness and reproducibility of the noncontact method with a small pilot study with 23 subjects. The presented method is the first demonstration of simultaneous BCG and PPG monitoring without wearing any extra equipment or marker by the subject.

Fiber bragg grating sensor based device for simultaneous measurement of respiratory and cardiac activities

This paper reports a novel optical ballistocardiography technique, which is non-invasive, for the simultaneous measurement of cardiac and respiratory activities using a Fiber Bragg Grating Heart Beat Device (FBGHBD). The unique design of FBGHBD offers additional capabilities such as monitoring nascent morphology of cardiac and breathing activity, heart rate variability, heart beat rhythm, etc., which can assist in early clinical diagnosis of many conditions associated with heart and lung malfunctioning. The results obtained from the FBGHBD positioned around the pulmonic area on the chest have been evaluated against an electronic stethoscope which detects and records sound pulses originated from the cardiac activity. In order to evaluate the performance of the FBGHBD, quantitative and qualitative studies have been carried out and the results are found to be reliable and accurate, validating its potential as a standalone medical diagnostic device. The developed FBGHBD is simple in design, robust, portable, EMI proof, shock proof and non-electric in its operation which are desired features for any clinical diagnostic tool used in hospital environment.

A Hidden Markov Model for Seismocardiography

We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and [Formula: see text], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services.

Robust heartbeat detection from in-home ballistocardiogram signals of older adults using a bed sensor

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.

Quantifying and Reducing Motion Artifacts in Wearable Seismocardiogram Measurements During Walking to Assess Left Ventricular Health

GOAL

Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subject's preferred pace) and moderately fast (1.34-1.45 m/s) speeds.

METHODS

We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking.

RESULTS

The EMD-based denoising approach provides a statistically significant increase in the signal-to-noise ratio of wearable SCG signals and also improves estimation of PEP during walking.

CONCLUSION

The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking.

SIGNIFICANCE

A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high-quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement-aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular system's response to stress (exercise), and thus provide a more holistic assessment of overall health.

J peak extraction from non-standard ballistocardiography data: a preliminary study

In recent years, several advanced algorithms based on clustering, multi-method or data fusion approaches have been proposed to estimate heartbeat intervals from non-standard ballistocardiography (BCG) data. These advanced algorithms generally have higher computational complexity than J-peak based algorithms. This fact motivated us to study the problem of extracting J peaks from non-standard BCG data, because if this extraction can be realized, then a low-complexity J-peak based algorithm can be used to fast estimate heartbeat intervals from non-standard BCG data. We found that most of the energy in J peaks is contained in a relatively narrow frequency band, called J-peak band, and that the heartbeat harmonics outside the J-peak band can cause the non-standard BCG waveform. According to these findings, a FIR linear phase filter with the J-peak band as its pass-band is proposed. The experimental result demonstrates the ability of the proposed filter to extract J peaks from non-standard BCG data.

Evaluation of ensemble averaging methods in 3D ballistocardiography

Ballistocardiography (BCG) is a non-invasive technique which measures the acceleration of a body induced by cardiovascular activity, namely the force exerted by the beating heart. Measuring a BCG in a gravity-free environment provides ideal conditions where the subject is completely decoupled from its environment. Furthermore, because gravity constrains the motion in two dimensions, the non-negligible accelerations taking place in the third dimension are lost. In every experimental situation, the measured BCG signal contains artifacts pertaining to different causes. One of them is the undesirable involuntary movements of the subject. Ensemble averaging (EA) tackles the issue of constructing a typical one cardiac cycle BCG signal which best represents a longer recording. The present work compares state-of-the-art EA methods and proposes two novel techniques, one taking into account the ECG sub-intervals and the other one based on Dynamic Time Warping. The effects of lung volume are also assessed.

Standing ballistocardiography measurements in microgravity

The performance and practicality of a scale-based ballistocardiogram (BCG) system for hemodynamic monitoring of astronauts on extended space missions was demonstrated. The system consists of a modified electronic weighing scale fitted with foot bindings to mechanically couple the subject to the scale. This system was tested on a recent series of parabolic flights in which scale-based and accelerometry-based free-floating BCG of 10 subjects was measured in microgravity. The signal-to-noise ratio (SNR) of the scale-based BCG was, on average, a factor of 2.1 (6.3 dB) higher than the free-floating method, suggesting that the tethered scale approach might be more robust in terms of signal quality. Additionally, this approach enables practical BCG-based hemodynamic monitoring in fractional-g environments, and on small space vehicles such as NASA's upcoming Orion capsule. The scale-based results in microgravity were also compared to ground measurements (1 g), where there was an average 38.7 ms RJ interval reduction from ground to microgravity environments that is consistent across 9 of 10 subjects. This phenomenon is likely due to the transient increase in venous return, and consequent decrease in pre-ejection period, experienced during the microgravity time intervals.

Unobtrusive Estimation of Cardiac Contractility and Stroke Volume Changes Using Ballistocardiogram Measurements on a High Bandwidth Force Plate

Unobtrusive and inexpensive technologies for monitoring the cardiovascular health of heart failure (HF) patients outside the clinic can potentially improve their continuity of care by enabling therapies to be adjusted dynamically based on the changing needs of the patients. Specifically, cardiac contractility and stroke volume (SV) are two key aspects of cardiovascular health that change significantly for HF patients as their condition worsens, yet these parameters are typically measured only in hospital/clinical settings, or with implantable sensors. In this work, we demonstrate accurate measurement of cardiac contractility (based on pre-ejection period, PEP, timings) and SV changes in subjects using ballistocardiogram (BCG) signals detected via a high bandwidth force plate. The measurement is unobtrusive, as it simply requires the subject to stand still on the force plate while holding electrodes in the hands for simultaneous electrocardiogram (ECG) detection. Specifically, we aimed to assess whether the high bandwidth force plate can provide accuracy beyond what is achieved using modified weighing scales we have developed in prior studies, based on timing intervals, as well as signal-to-noise ratio (SNR) estimates. Our results indicate that the force plate BCG measurement provides more accurate timing information and allows for better estimation of PEP than the scale BCG (r² = 0.85 vs.r² = 0.81) during resting conditions. This correlation is stronger during recovery after exercise due to more significant changes in PEP (r² = 0.92). The improvement in accuracy can be attributed to the wider bandwidth of the force plate. ∆SV (i.e., changes in stroke volume) estimations from the force plate BCG resulted in an average error percentage of 5.3% with a standard deviation of ±4.2% across all subjects. Finally, SNR calculations showed slightly better SNR in the force plate measurements among all subjects but the small difference confirmed that SNR is limited by motion artifacts rather than instrumentation.

Physiological Signal Monitoring Bed for Infants Based on Load-Cell Sensors

Ballistocardiographs (BCGs), which record the mechanical activity of the heart, have been a subject of interest for several years because of their advantages in providing unobtrusive physiological measurements. BCGs could also be useful for monitoring the biological signals of infants without the need for physical confinement. In this study, we describe a physiological signal monitoring bed based on load cells and assess an algorithm to extract the heart rate and breathing rate from the measured load-cell signals. Four infants participated in a total of 13 experiments. As a reference signal, electrocardiogram and respiration signals were simultaneously measured using a commercial device. The proposed automatic algorithm then selected the optimal sensor from which to estimate the heartbeat and respiration information. The results from the load-cell sensor signals were compared with those of the reference signals, and the heartbeat and respiration information were found to have average performance errors of 2.55% and 2.66%, respectively. The experimental results verify the positive feasibility of BCG-based measurements in infants.

MRI-based aortic blood flow model in 3D ballistocardiography

Ballistocardiography (BCG) is a non-invasive technique which measures the acceleration of a body induced by cardiovascular activity, namely the force exerted by the beating heart. A one dimensional aortic flow model based on the transmission lines theory is developped and applied to the simulation of three dimensional BCG. A four-element Windkessel model is used to generate the pressure-wave. Using transverse MRI slices of a human subject, a reconstruction of the aorta allows the extraction of parameters used to relate the local change in mass of the 1D flow model to 3D acceleration BCG. Simulated BCG curves are then compared qualitatively with the ensemble average curves of the same subject recorded in sustained microgravity. Confirming previous studies, the main features of the y-axis are well simulated. The simulated z-axis, never attempted before, shows important similarities. The simulated x-axis is less faithful and suggests the presence of reflections.

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.

Ballistocardiogram of baby during sleep

Ballistocardiogram (BCG), which displays the mechanical activity of heart, has been a subject of interest for several years due to its advantages in taking unobtrusive physiological measurements. In the field of sleep science, researchers actively study sleep architecture and clinically apply various sleep-related conditions through BCG-derived biological information such as the heartbeat, respiration and body movements of subjects. However, most of these studies have involved only adults. This area of research may be even more important with babies to monitor their biological signals without confinement. For this reason, we developed a physiological signal monitoring bed for baby by using a load cell. Heartbeat and respiration information was assessed with average respective performance errors of 1.53% and 2.53% compared to commercial equipment. The results showed the possibility of applying BCG technology to baby. Therefore, we expect that BCG-derived signals can be extensively applied to analyze sleep architecture and clinical applications in baby as they are with adults.

Ballistocardiographic Artifacts in PPG Imaging

OBJECTIVE

Photoplethysmography (PPG) is a noninvasive technique to measure the blood-volume pulse and derive various vital signs. Camera-based PPG imaging was recently proposed for clinical microvascular assessment, but motion robustness is still an issue for this technique. Our study aims to quantify cardiac-related, i.e., ballistocardiographic (BCG), motion as a source of artifacts in PPG imaging.

METHODS

In this paper, using the human head as a relevant region of interest, the amplitude of BCG-artifacts was modeled for a Lambertian surface illuminated by a light source. To derive peak-to-peak head displacements for the model, we recorded, on 54 subjects, PPG and inertial sensor data at the pulse and cranial vertex. We simulated the effect of light source location at a mesh representation of a human face and conducted additional experiments on a real subject.

RESULTS

Under nonorthogonal illumination, the relative strength of the BCG artifacts is strong enough, compared to the amplitude of PPG signals, to compromise PPG imaging in realistic scenarios. Particularly affected are the signals obtained in the nongreen part of the spectrum and/or when the incident angle at the skin surface exceeds 45 (°).

CONCLUSION

From the model and an additional experiment conducted on real skin, we were able to prove that homogenous and orthogonal illumination is a means to minimize the problem.

SIGNIFICANCE

Our illumination recommendation provides a simple and effective means to improve the validity of remote PPG-imagers. We hope that it helps to prevent mistakes currently seen in many publications on remote PPG.

Improvement of force-sensor-based heart rate estimation using multichannel data fusion

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).

Reference-free harmonic regression technique to remove EEG-fMRI ballistocardiogram artifacts

Obtaining high quality electroencephalogram (EEG) data simultaneously with functional MRI (fMRI) recordings is increasingly relevant for the study of cognitive and clinical brain states - as EEG-fMRI offers uniquely high spatiotemporal resolution imaging of brain activity. However, the utility of this technique is limited by ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation and head movement inside the magnetic field. In this paper, we introduce a novel model-based harmonic regression technique to remove BCG artifacts from EEG recorded in the MR scanner. Our technique uses physically motivated parametric models of the BCG artifact and the true EEG signal, and incorporates maximum likelihood approaches to identify model parameters, estimate and subtract the BCG from corrupted EEG measurements. We show that this method effectively removes BCG artifacts from EEG recorded in the MR scanner, restores simulated oscillatory signatures and enables over 20-fold improvement in SNR in bands of interest. Further, unlike common BCG removal techniques that rely on cardiac or motion reference signals, our approach is reference-free and thus is useful when reference signals are corrupted or difficult to acquire.

Estimating the center of mass of a free-floating body in microgravity

This paper addresses the issue of estimating the position of the center of mass (CoM) of a free-floating object of unknown mass distribution in microgravity using a stereoscopic imaging system. The method presented here is applied to an object of known mass distribution for validation purposes. In the context of a study of 3-dimensional ballistocardiography in microgravity, and the elaboration of a physical model of the cardiovascular adaptation to weightlessness, the hypothesis that the fluid shift towards the head of astronauts induces a significant shift of their CoM needs to be tested. The experiments were conducted during the 57th parabolic flight campaign of the European Space Agency (ESA). At the beginning of the microgravity phase, the object was given an initial translational and rotational velocity. A 3D point cloud corresponding to the object was then generated, to which a motion-based method inspired by rigid body physics was applied. Through simulations, the effects of the centroid-to-CoM distance and the number of frames of the sequence are investigated. In experimental conditions, considering the important residual accelerations of the airplane during the microgravity phases, CoM estimation errors (16 to 76 mm) were consistent with simulations. Overall, our results suggest that the method has a good potential for its later generalization to a free-floating human body in a weightless environment.

Cardiovascular changes in parabolic flights assessed by ballistocardiography

This paper presents a comparison of the cardiovascular changes observed in microgravity as compared to ground based measurements. The ballistocardiogram (BCG), the electrocardiogram (ECG) and the transthoracic impedance cardiogram (ICG) were recorded on five healthy subjects during the 57th-European Space Agency (ESA) parabolic flight campaign. BCG is analyzed though its most characteristic wave, the IJ wave complex that can be identified along the longitudinal component of BCG and which has been demonstrated to be linked to cardiac ejection. The timings between the contraction of the heart and the ejection of blood in the aorta are analyzed via the time delay between the R-wave of the ECG and the I and J-waves of BCG (RI and RJ intervals respectively). Our results show that the IJ complex presents a larger amplitude in weightlessness and suggest that stroke volume (SV) increases in microgravity. We assume that ballistocardiography is an efficient method to assess the ventricular performance.

Quantifying and Reducing Posture-Dependent Distortion in Ballistocardiogram Measurements

Ballistocardiography is a noninvasive measurement of the mechanical movement of the body caused by cardiac ejection of blood. Recent studies have demonstrated that ballistocardiogram (BCG) signals can be measured using a modified home weighing scale and used to track changes in myocardial contractility and cardiac output. With this approach, the BCG can potentially be used both for preventive screening and for chronic disease management applications. However, for achieving high signal quality, subjects are required to stand still on the scale in an upright position for the measurement; the effects of intentional (for user comfort) or unintentional (due to user error) modifications in the position or posture of the subject during the measurement have not been investigated in the existing literature. In this study, we quantified the effects of different standing and seated postures on the measured BCG signals, and on the most salient BCG-derived features compared to reference standard measurements (e.g., impedance cardiography). We determined that the standing upright posture led to the least distorted signals as hypothesized, and that the correlation between BCG-derived timing interval features (R-J interval) and the preejection period, PEP (measured using ICG), decreased significantly with impaired posture or sitting position. We further implemented two novel approaches to improve the PEP estimates from other standing and sitting postures, using system identification and improved J-wave detection methods. These approaches can improve the usability of standing BCG measurements in unsupervised settings (i.e., the home), by improving the robustness to nonideal posture, as well as enabling high-quality seated BCG measurements.

Ballistocardiogram as Proximal Timing Reference for Pulse Transit Time Measurement: Potential for Cuffless Blood Pressure Monitoring

GOAL

We tested the hypothesis that the ballistocardiogram (BCG) waveform could yield a viable proximal timing reference for measuring pulse transit time (PTT).

METHODS

From 15 healthy volunteers, we measured PTT as the time interval between BCG and a noninvasively measured finger blood pressure (BP) waveform. To evaluate the efficacy of the BCG-based PTT in estimating BP, we likewise measured pulse arrival time (PAT) using the electrocardiogram (ECG) as proximal timing reference and compared their correlations to BP.

RESULTS

BCG-based PTT was correlated with BP reasonably well: the mean correlation coefficient (r ) was 0.62 for diastolic (DP), 0.65 for mean (MP), and 0.66 for systolic (SP) pressures when the intersecting tangent method was used as distal timing reference. Comparing four distal timing references (intersecting tangent, maximum second derivative, diastolic minimum, and systolic maximum), PTT exhibited the best correlation with BP when the systolic maximum method was used (mean r value was 0.66 for DP, 0.67 for MP, and 0.70 for SP). PTT was more strongly correlated with DP than PAT regardless of the distal timing reference: mean r value was 0.62 versus 0.51 (p = 0.07) for intersecting tangent, 0.54 versus 0.49 (p = 0.17) for maximum second derivative, 0.58 versus 0.52 (p = 0.37) for diastolic minimum, and 0.66 versus 0.60 (p = 0.10) for systolic maximum methods. The difference between PTT and PAT in estimating DP was significant (p = 0.01) when the r values associated with all the distal timing references were compared altogether. However, PAT appeared to outperform PTT in estimating SP ( p = 0.31 when the r values associated with all the distal timing references were compared altogether).

CONCLUSION

We conclude that BCG is an adequate proximal timing reference in deriving PTT, and that BCG-based PTT may be superior to ECG-based PAT in estimating DP.

SIGNIFICANCE

PTT with BCG as proximal timing reference has potential to enable convenient and ubiquitous cuffless BP monitoring.

Unified frame of reference improves inter-subject variability of seismocardiograms

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.

A method of detecting heartbeat locations in the ballistocardiographic signal from the fiber-optic vital signs sensor

We present a flexible, easy-to-expand digital signal processing method for detecting heart rate (HR) for cardiac vibration signals of fiber Bragg grating (FBG) sensor. The FBG-based method of measuring HR is possible to use during the magnetic resonance imaging procedure, which is its unique advantage. Our goal was to design a detection method with plurality of parameters and to subject these parameters to genetic algorithm optimization technique. In effect, we arrived at a method that is well able to deal with much distorted signals with low SNR. We proved that the method we developed allows automatic adjustment to the shape of the waves of signal carrying useful information about the moments of heartbeat. Thus, we can easily adapt our technique to the analysis of signals, which contains information on HR, from sensors employing different techniques of strain detection. The proposed method has the capabilities of analyzing signals in semi-real-time (online) with beat-to-beat resolution, significantly low delay, and negligible computational power requirements. We verified our method on recordings in a group of seven subjects. Verification included over 6000 heartbeats (82 min 47 s of recordings). The root-mean-square error of our method does not exceed 6.0 bpm.

Beat-by-Beat Quantification of Cardiac Cycle Events Detected From Three-Dimensional Precordial Acceleration Signals

The vibrations produced by the cardiovascular system that are coupled to the precordium can be noninvasively detected using accelerometers. This technique is called seismocardiography. Although clinical applications have been proposed for seismocardiography, the physiology underlying the signal is still not clear. The relationship of seismocardiograms of on the back-to-front axis and cardiac events is fairly well known. However, the 3-D seismocardiograms detectable with modern accelerometers have not been quantified in terms of cardiac cycle events. A major reason for this might be the degree of intersubject variability observed in 3-D seismocardiograms. We present a method to quantify 3-D seismocardiography in terms of cardiac cycle events. First, cardiac cycle events are identified from the seismocardiograms, and then, assigned a number based on the location in which the corresponding event was found. 396 cardiac cycle events from 9 healthy subjects and 120 cardiac cycle events from patients suffering from atrial flutter were analyzed. Despite the weak intersubject correlation of the waveforms (0.05, 0.27, and 0.15 for the x-, y-, and z-axes, respectively), the present method managed to find latent similarities in the seismocardiograms of healthy subjects. We observed that in healthy subjects the distribution of cardiac cycle event coordinates was centered on specific locations. These locations were different in patients with atrial flutter. The results suggest that spatial distribution of seismocardiographic cardiac cycle events might be used to discriminate healthy individuals and those with a failing heart.

Three dimensional Ballistocardiogram and Seismocardiogram: what do they have in common?

3D-body accelerations, i.e. Ballistocardiograms (BCG) and Seismocardiograms (SCG), ECG and Impedance-cardiograms (ICG) were recorded on healthy volunteers participating to the European Space Agency (ESA) 59th parabolic flight campaign. In the present paper we document the similarities and differences that can be seen in the seismo- and ballisto-cardiogram signals in different positions (standing and supine) under normal gravity condition as well as during the weightlessness phases (0G) of a parabolic flight. Our results demonstrate that SCG and BCG both present a similar three dimensional (3D) nature, with components of the BCG having lower frequency content than the SCG. The recordings performed in the 0G environment are the one with the smoothest shape and largest maximum magnitude of the Force vector. The differences seen between SCG and BCG stress further the importance for the need of using different nomenclature for the identification of peaks in both signals.

A novel system identification technique for improved wearable hemodynamics assessment

Recent advances have led to renewed interest in ballistocardiography (BCG), a noninvasive measure of the small movements of the body due to cardiovascular events. A broad range of platforms have been developed and verified for BCG measurement including beds, chairs, and weighing scales: while the body is coupled to such a platform, the cardiogenic movements are measured. Wearable BCG, measured with an accelerometer affixed to the body, may enable continuous, or more regular, monitoring during the day; however, the signals from such wearable BCGs represent local or distal accelerations of skin and tissue rather than the whole body. In this paper, we propose a novel method to reconstruct the BCG measured with a weighing scale (WS BCG) from a wearable sensor via a training step to remove these local effects. Preliminary validation of this method was performed with 15 subjects: the wearable sensor was placed at three locations on the surface of the body while WS BCG measurements were recorded simultaneously. A regularized system identification approach was used to reconstruct the WS BCG from the wearable BCG. Preliminary results suggest that the relationship between local and central disturbances is highly dependent on both the individual and the location where the accelerometer is placed on the body and that these differences can be resolved via calibration to accurately measure changes in cardiac output and contractility from a wearable sensor. Such measurements could be highly effective, for example, for improved monitoring of heart failure patients at home.

[De-noising method research of ballistocardiogram signal]

Ballistocardiogram (BCG) signal is a physiological signal, reflecting heart mechanical status. It can be measured without any electrodes touching subject's body surface and can realize physiological monitoring ubiquitously. However, BCG signal is so weak that it would often be interferred by superimposed noises. For measuring BCG signal effectively, we proposed an approach using joint time-frequency distribution and empirical mode decomposition (EMD) for BCG signal denoising. We set up an adaptive optimal kernel for BCG signal and extracted BCG signals components using it. Then we de-noised the BCG signal by combing empirical mode decomposition with it. Simulation results showed that the proposed method overcome the shortcomings of empirical mode decomposition for the signals with identical frequency content at different times, realized the filtering for BCG signal and also reconstructed the characteristics of BCG.