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.

DR.BEAT: Initial Functional Testing of a BCG Wearable Prototype for Recording Ballistocardiographic Signals.

DR.BEAT ("Digital Research on Ballistocardiography for Extraterrestrial And Terrestrial use") develops a miniaturized sensor system with signal processing to interpret ballistocardiographic signals and implements an application oriented user interface. Presented is a breadboard prototype's functional tests with regard to data completeness and plausibility. The analysis confirmed a reliability of 99.99995% over the tests and the signals displayed the expected heart-specific characteristics. These results support the ethical justifiability of an initial study.

Cardiac output estimation using ballistocardiography: a feasibility study in healthy subjects.

There is no reliable automated non-invasive solution for monitoring circulation and guiding treatment in prehospital emergency medicine. Cardiac output (CO) monitoring might provide a solution, but CO monitors are not feasible/practical in the prehospital setting. Non-invasive ballistocardiography (BCG) measures heart contractility and tracks CO changes. This study analyzed the feasibility of estimating CO using morphological features extracted from BCG signals. In 20 healthy subjects ECG, carotid/abdominal BCG, and invasive arterial blood pressure based CO were recorded. BCG signals were adaptively processed to isolate the circulatory component from carotid (CCc) and abdominal (CCa) BCG. Then, 66 features were computed on a beat-to-beat basis to characterize amplitude/duration/area/length of the fluctuation in CCc and CCa. Subjects' data were split into development set (75%) to select the best feature subset with which to build a machine learning model to estimate CO and validation set (25%) to evaluate model's performance. The model showed a mean absolute error, percentage error and 95% limits of agreement of 0.83 L/min, 30.2% and - 2.18-1.89 L/min respectively in the validation set. BCG showed potential to reliably estimate/track CO. This method is a promising first step towards an automated, non-invasive and reliable CO estimator that may be tested in prehospital emergencies.

Identifying an Appropriate Area to Facilitate the Cardiorespiratory Measurement during Sleep Monitoring.

Measuring cardiorespiratory parameters in sleep, using non-contact sensors and the Ballistocardiography technique has received much attention due to the low-cost, unobtrusive, and non-invasive method. Designing a user-friendly, simple-to-use, and easy-to-deployment preserving less error-prone remains open and challenging due to the complex morphology of the signal. In this work, using four forcesensitive resistor sensors, we conducted a study by designing four distributions of sensors, in order to simplify the complexity of the system by identifying the region of interest for heartbeat and respiration measurement. The sensors are deployed under the mattress and attached to the bed frame without any interference with the subjects. The four distributions are combined in two linear horizontal, one linear vertical, and one square, covering the influencing region in cardiorespiratory activities. We recruited 4 subjects and acquired data in four regular sleeping positions, each for a duration of 80 seconds. The signal processing was performed using discrete wavelet transform bior 3.9 and smooth level of 4 as well as bandpass filtering. The results indicate that we have achieved the mean absolute error of 2.35 and 4.34 for respiration and heartbeat, respectively. The results recommend the efficiency of a triangleshaped structure of three sensors for measuring heartbeat and respiration parameters in all four regular sleeping positions.

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%.

Apple Watch Sleep and Physiological Tracking Compared to Clinically Validated Actigraphy, Ballistocardiography and Polysomnography.

This paper investigates the performance of the latest Apple Watch (Series 8, released September 2022) in comparison with research grade devices. The Apple Watch was compared to wrist worn actigraphy, non-contact ballistocardiography (BCG) placed in the bed and evaluated with polysomnography (PSG) as a reference system. Sleep analysis and individual cardiorespiratory parameters were measured from the Apple Watch. The Apple Watch performed well for identifying sleep-wake states but had difficulty identifying the sleep stages compared to the reference PSG system. Physiological parameters obtained from the Apple Watch compared well with measurements of the other devices in the study.Clinical Relevance- Consumer devices are readily available and inexpensive compared to clinical devices. A consumer device that can provide accurate physiological data equivalent to a clinical device would let researchers and clinicians collect data without the intrusive nature of a clinical device.

DR.BEAT: First Insights into a Study to Collect Baseline BCG Data with a Sensor-Based Wearable Prototype in Heart-Healthy Adults.

The DR.BEAT project aims at the further development of a measurement system for recording ballistocardiographic signals into a body-worn sensor system combined with extensive signal processing, data evaluation and visualization. With a first breadboard prototype, an explorative feasibility study for acquiring initial signals of healthy cardiac activity in adults was performed. This paper briefly presents the DR.BEAT project, the breadboard prototype, the study conducted, and initial insights into the study results. The signals obtained in the study exhibit the seismocardiographic characteristics as reported in the literature and form the basis for further development of the hardware as well as the pre-processing and automated analysis algorithms in the DR.BEAT project.Clinical Relevance- The characteristics of ballisto- and seismocardiographic signals allow to infer about the mechanical work of the heart. The development of a body-worn sensor system to record ballisto- and seismocardiographic signals, compact enough for everyday wear, enables the acquisition of heart-specific parameters in terrestrial as well as extraterrestrial application scenarios. Combined with extensive signal analysis and visualization, it holds the potential to monitor heart health in a variety of contexts and support its maintenance and improvement.

Classification of health deterioration by geometric invariants.

BACKGROUND AND OBJECTIVES: Prediction of patient deterioration is essential in medical care, and its automation may reduce the risk of patient death. The precise monitoring of a patient's medical state requires devices placed on the body, which may cause discomfort. Our approach is based on the processing of long-term ballistocardiography data, which were measured using a sensory pad placed under the patient's mattress. METHODS: The investigated dataset was obtained via long-term measurements in retirement homes and intensive care units (ICU). Data were measured unobtrusively using a measuring pad equipped with piezoceramic sensors. The proposed approach focused on the processing methods of the measured ballistocardiographic signals, Cartan curvature (CC), and Euclidean arc length (EAL). RESULTS: For analysis, 218,979 normal and 216,259 aberrant 2-second samples were collected and classified using a convolutional neural network. Experiments using cross-validation with expert threshold and data length revealed the accuracy, sensitivity, and specificity of the proposed method to be 86.51 CONCLUSIONS: The proposed method provides a unique approach for an early detection of health concerns in an unobtrusive manner. In addition, the suitability of EAL over the CC was determined.

On-Site Visualization of Ballistocardiography Data.

We describe the background, features and functions of a custom application for the acquisition, live presentation, and convenient recording of ballistocardiography data acquired by external accelerometric sensors.

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

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

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions.

The cardiac function is influenced by respiration. In particular, various parameters such as cardiac time intervals and the stroke volume are modulated by respiratory activity. It has long been recognized that cardio-respiratory interactions modify the morphology of cardio-mechanical signals, e.g., phonocardiogram, seismocardiogram (SCG), and ballistocardiogram. Forcecardiography (FCG) records the weak forces induced on the chest wall by the mechanical activity of the heart and lungs and relies on specific force sensors that are capable of monitoring respiration, infrasonic cardiac vibrations, and heart sounds, all simultaneously from a single site on the chest. This study addressed the changes in FCG heartbeat morphology caused by respiration. Two respiratory-modulated parameters were considered, namely the left ventricular ejection time (LVET) and a morphological similarity index (MSi) between heartbeats. The time trends of these parameters were extracted from FCG signals and further analyzed to evaluate their consistency within the respiratory cycle in order to assess their relationship with the breathing activity. The respiratory acts were localized in the time trends of the LVET and MSi and compared with a reference respiratory signal by computing the sensitivity and positive predictive value (PPV). In addition, the agreement between the inter-breath intervals estimated from the LVET and MSi and those estimated from the reference respiratory signal was assessed via linear regression and Bland-Altman analyses. The results of this study clearly showed a tight relationship between the respiratory activity and the considered respiratory-modulated parameters. Both the LVET and MSi exhibited cyclic time trends that remarkably matched the reference respiratory signal. In addition, they achieved a very high sensitivity and PPV (LVET: 94.7% and 95.7%, respectively; MSi: 99.3% and 95.3%, respectively). The linear regression analysis reported almost unit slopes for both the LVET (R2 = 0.86) and MSi (R2 = 0.97); the Bland-Altman analysis reported a non-significant bias for both the LVET and MSi as well as limits of agreement of ±1.68 s and ±0.771 s, respectively. In summary, the results obtained were substantially in line with previous findings on SCG signals, adding to the evidence that FCG and SCG signals share a similar information content.

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.

Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram.

Cuffless blood pressure measurement enables unobtrusive and continuous monitoring that can be integrated with wearable devices to extend healthcare to non-hospital settings. Most of the current research has focused on the estimation of blood pressure based on pulse transit time or pulse arrival time using ECG or peripheral cardiac pulse signals as proximal time references. This study proposed the use of a phonocardiogram (PCG) and ballistocardiogram (BCG), two signals detected noninvasively, to estimate systolic blood pressure (SBP). For this, the PCG and the BCG were simultaneously measured in 21 volunteers in the rest, activity, and post-activity conditions. Different features were considered based on the relationships between these signals. The intervals between S1 and S2 of the PCG and the I, J, and K waves of the BCG were statistically analyzed. The IJ and JK slopes were also estimated as additional features to train the machine-learning algorithm. The intervals S1-J, S1-K, S1-I, J-S2, and I-S2 were negatively correlated with changes in SBP (p-val < 0.01). The features were used as explanatory variables for a regressor based on the Random Forest. It was possible to estimate the systolic blood pressure with a mean error of 3.3 mmHg with a standard deviation of ± 5 mmHg. Therefore, we foresee that this proposal has potential applications for wearable devices that use low-cost embedded systems.

Effect of 4-day Online Breath Meditation Workshop on Ballistocardiography-based Sleep and Cardiac Health Assessments among Medical Professionals of a Tertiary Care Hospital in North India during COVID-19.

BACKGROUND: Medical professionals (MPs) are facing stress, sleep deprivation, and burnout due to pandemic-related high patient inflow and consistent work shifts. Yoga and meditation are feasible, cost-effective, evidence-based, and well-accepted tools having multifold mental and physical health benefits. DESIGN: In this ongoing open-label single-arm trial, we assessed changes in sleep, heart rate variability (HRV), and vitals before and after a 4-day online breath meditation workshop (OBMW) among 41 MPs at a tertiary care hospital in northern India during COVID-19 pandemic. METHODS: Outcomes were assessed at baseline and after the 4-day workshop using a ballistocardiography-based contactless health monitoring device. The workshop was conducted online. Two participants were excluded due to a lack of adherence. RESULTS: A highly significant increase was seen in total sleep duration (p = 0.000) and duration of deep sleep (p = 0.001), rapid eye movement (REM) sleep (p = 0.000), and light sleep (p = 0.032). HRV outcomes of the standard deviation of normal-to-normal R-R intervals (SDNN) and root mean square of successive differences between adjacent normal heartbeat (RMSSD) also improved significantly (p = 0.000) while heart rate reduced significantly (p = 0.001). No significant change was observed in breath rate, total time awake, or in the low-frequency by high-frequency (LF/HF) spectrum of HRV. CONCLUSION: Four days of OBMW improved sleep and HRV among MPs, strengthening the fact that yoga and meditation can help induce psychophysical relaxation and prove to be an effective tool to combat stress and sleep deprivation. As the stakeholders in patient care, that is, MPs are healthy, it will further improve patient care and reduce the chance of medical errors.

Classification of Sleep-Wake State in Ballistocardiogram system based on Deep Learning.

Sleep state classification is essential for managing and comprehending sleep patterns, and it is usually the first step in identifying sleep disorders. Polysomnography (PSG), the gold standard, is intrusive and inconvenient for regular/long-term sleep monitoring. Many sleep-monitoring techniques have recently seen a resurgence as a result of the rise of neural networks and advanced computing. Ballistocardiography (BCG) is an example of such a technique, in which vitals are monitored in a contactless and unobtrusive manner by measuring the body's reaction to cardiac ejection forces. A Multi-Headed Deep Neural Network is proposed in this study to accurately classify sleep-wake state and predict sleep-wake time using BCG sensors. This method achieves a 95.5% sleep-wake classification score. Two studies were conducted in a controlled and uncontrolled environment to assess the accuracy of sleep-awake time prediction. Sleep-awake time prediction achieved an accuracy score of 94.16% in a controlled environment on 115 subjects and 94.90% in an uncontrolled environment on 350 subjects. The high accuracy and contactless nature make this proposed system a convenient method for long-term monitoring of sleep states, and it may also aid in identifying sleep stages and other sleep-related disorders. Clinical Relevance- Current sleep-wake state classification methods, such as actigraphy and polysomnography, necessitate patient contact and a high level of patient compliance. The proposed BCG method was found to be comparable to the gold standard PSG and most wearable actigraphy techniques, and also represents an effective method of contactless sleep monitoring. As a result, clinicians can use it to easily screen for sleep disorders such as dyssomnia and sleep apnea, even from the comfort of one's own home.

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.

Estimation of the respiratory rate from ballistocardiograms using the Hilbert transform.

BACKGROUND: Measuring the respiratory rate is usually associated with discomfort for the patient due to contact sensors or a high time demand for healthcare personnel manually counting it. METHODS: In this paper, two methods for the continuous extraction of the respiratory rate from unobtrusive ballistocardiography signals are introduced. The Hilbert transform is used to generate an amplitude-invariant phase signal in-line with the respiratory rate. The respiratory rate can then be estimated, first, by using a simple peak detection, and second, by differentiation. RESULTS: By analysis of a sleep laboratory data set consisting of nine records of healthy individuals lasting more than 63 h and including more than 59,000 breaths, a mean absolute error of as low as 0.7 BPM for both methods was achieved. CONCLUSION: The results encourage further assessment for hospitalised patients and for home-care applications especially with patients suffering from diseases of the respiratory system like COPD or sleep apnoea.

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.

Quantitative Analysis Using Consecutive Time Window for Unobtrusive Atrial Fibrillation Detection Based on Ballistocardiogram Signal.

Atrial fibrillation (AF) is the most common clinically significant arrhythmia; therefore, AF detection is crucial. Here, we propose a novel feature extraction method to improve AF detection performance using a ballistocardiogram (BCG), which is a weak vibration signal on the body surface transmitted by the cardiogenic force. In this paper, continuous time windows (CTWs) are added to each BCG segment and recurrence quantification analysis (RQA) features are extracted from each time window. Then, the number of CTWs is discussed and the combined features from multiple time windows are ranked, which finally constitute the CTW-RQA features. As validation, the CTW-RQA features are extracted from 4000 BCG segments of 59 subjects, which are compared with classical time and time-frequency features and up-to-date energy features. The accuracy of the proposed feature is superior, and three types of features are fused to obtain the highest accuracy of 95.63%. To evaluate the importance of the proposed feature, the fusion features are ranked using a chi-square test. CTW-RQA features account for 60% of the first 10 fusion features and 65% of the first 17 fusion features. It follows that the proposed CTW-RQA features effectively supplement the existing BCG features for AF detection.

Contactless continuous heart rate monitoring system using ballistocardiography.

Cardiovascular disease is the number one cause of death in the world and is a serious problem. In the case of cardiopulmonary arrest due to myocardial infarction, the survival rate is as low as 13.3% one month after resuscitation, which birthed the need for continuous heart monitoring. In this study, we develop a Ballistocardiogram (BCG) measurement system using a load cell installed on a chair and a heart rate estimation algorithm that is robust to waveform changes, with the aim of constructing a non-contact heart rate acquisition system. The proposed system was evaluated by utilizing data obtained from 13 healthy subjects and 1 subject with abnormal ECG who were simultaneously measured with ECG. The output of the BCG system was confirmed to change with the same period as the ECG data obtained as the correct answer, and the synchronization of the R-peak positions was confirmed for all cases. As a result of comparing the heart rate intervals estimated from BCG and those obtained from ECG, it was confirmed that the same heart rate variability (HRV) features could be obtained even for abnormal ECG subject.