Ballistocardiography Research Articles

The kinocardiograph for assessment of fluid status in patients with acute decompensated heart failure

Abstract Funding Acknowledgements Type of funding sources: None. Aims To improve telemonitoring strategies in heart failure patients, there is a need for novel non-obtrusive sensors which monitor parameters closely related to intracardiac filling pressures. This proof-of-concept study aims to evaluate the responsiveness of cardiac kinetic energy (KE) measured with the Kinocardiograph (KCG), consisting of a seismocardiographic (SCG) sensor and a ballistocardiographic (BCG) sensor, during treatment of patients with acute decompensated heart failure (ADHF). Methods Eleven patients with ADHF who were hospitalized for treatment with intravenous diuretics, received daily KCG measurements. The KCG measurements were compared to the diameter of the inferior vena cava (IVC) and body weight. Follow-up stopped at discharge, i.e. in the recompensated state. Results Median (IQR) weight and IVC diameter decreased significantly after diuretic treatment (weight 74.5 (67.6–98.7) to 73.3 (66.7-95.6) kg, p=0.003; IVC diameter 2.47 (2.33–2.99) to 1.78 (1.65–2.47) cm, p=0.03). In contrast with BCG measurements, significant changes in median KE measured with SCG were observed during the passive filling phase of the diastole (0.48 (0.39–0.60) to 0.69 (0.56-0.84), p=0.026), the active filling phase of the diastole (0.38 (0.30–0.61) to 0.31 (0.09–0.47), p=0.016)) and the ratio between the passive and active filling phase (2.76 (1.68–5.30) to 5.02 (3.13–10.17), p=0.006). Systolic KE did not show significant changes. Conclusion KE measured with the KCG using SCG is highly responsive to changes in fluid status. Future research is needed to confirm its accuracy in a larger study population, and specifically its application for detection of clinical deterioration in the home-environment.

Non-invasive cardiac kinetic energy distribution: a new marker of heart failure with impaired ejection fraction (KINO-HF).

Heart failure (HF) remains a major cause of mortality, morbidity, and poor quality of life. 44% of HF patients present impaired left ventricular ejection fraction (LVEF). Kinocardiography (KCG) technology combines ballistocardiography (BCG) and seismocardiography (SCG). It estimates myocardial contraction and blood flow through the cardiac chambers and major vessels through a wearable device. Kino-HF sought to evaluate the potential of KCG to distinguish HF patients with impaired LVEF from a control group. Successive patients with HF and impaired LVEF (iLVEF group) were matched and compared to patients with normal LVEF ≥ 50% (control). A 60 s KCG acquisition followed cardiac ultrasound. The kinetic energy from KCG signals was computed in different phases of the cardiac cycle () as markers of cardiac mechanical function. Thirty HF patients (67 [59; 71] years, 87% male) were matched with 30 controls (64.5 [49; 73] years, 87% male). SCG , BCG , BCG were lower in HF than controls (p < 0.05), while SCG was similar. Furthermore, a lower SCG was associated with an increased mortality risk during follow-up. KINO-HF demonstrates that KCG can distinguish HF patients with impaired systolic function from a control group. These favorable results warrant further research on the diagnostic and prognostic capabilities of KCG in HF with impaired LVEF.Clinical Trial Registration: NCT03157115.

Wearable cardiorespiratory monitoring with stretchable elastomer optical fiber.

This work presents a stretchable elastomer optical fiber sensor incorporated into a belt for respiratory rate (RR) and heart rate (HR) monitoring. Different materials and shapes of prototypes designed were tested in terms of performance and the best choice was identified. The optimal sensor was tested by 10 volunteers to evaluate the performance. The proposed elastomer optical fiber sensor can achieve simultaneous measurement of RR and HR in different body positions, and also ballistocardiography (BCG) signal measurement in the lying position. The sensor has good accuracy and stability, with maximum errors of 1 bpm and 3 bpm for RR and HR, respectively, and average weighted mean absolute percentage error (MAPE) of 5.25% and root mean square error (RMSE) of 1.28 bpm. Moreover, the results of the Bland-Altman method showed good agreement of the sensor with manual counting of RR and with electrocardiogram (ECG) measurements of HR.

Feasibility of a new homebased ballistocardiographic tool for sleep-assessment in a real-life context among workers.

There is a need for simple and suitable tools for assessing sleep in a natural home environment. This study explores the feasibility in terms of implementation and acceptability of a new homebased ballistocardiographic (BCG) tool for objective sleep-assessment in a real-life context. The participants included thirty-nine workers, taking part in two seven nights' sleep-assessment periods. Objective data regarding sleep was collected with BCG. Subjective data regarding sleep was collected with a sleep diary. Implementation was analysed by determining the number of nights with usable signal quality and comparing with the total number of potential nights and by exploring associations between objective and subjective sleep data. Acceptability was analysed by categorizing the participants' experiences of how the BCG tool impacted the sleep. In terms of implementation, usable BCG data increased from 40% at assessment phase 1 to 70% during assessment phase 2. Moreover, in assessment phase 2, there was a significant moderate correlation between the 'time in bed' assessed by the BCG and in sleep diary by participants in the first five nights. In terms of acceptability, almost one third of the participants did not experience any impact of the BCG on the sleep. Two participants experienced a major negative impact on the sleep. This study indicates that the novel BCG tool could be feasible for objective assessing of sleep in workers natural home-environment in the future, but there is still a need for development of the BCG both regarding technology and implementation process.

Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals.

Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments. Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers. Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ). Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.

Automated Hypertension Detection Using ConvMixer and Spectrogram Techniques with Ballistocardiograph Signals.

Blood pressure is the pressure exerted by the blood in the veins against the walls of the veins. If this value is above normal levels, it is known as high blood pressure (HBP) or hypertension (HPT). This health problem which often referred to as the "silent killer" reduces the quality of life and causes severe damage to many body parts in various ways. Besides, its mortality rate is very high. Hence, rapid and effective diagnosis of this health problem is crucial. In this study, an automatic diagnosis of HPT has been proposed using ballistocardiography (BCG) signals. The BCG signals were transformed to the time-frequency domain using the spectrogram method. While creating the spectrogram images, parameters such as window type, window length, overlapping rate, and fast Fourier transform size were adjusted. Then, these images were classified using ConvMixer architecture, similar to vision transformers (ViT) and multi-layer perceptron (MLP)-mixer structures, which have attracted a lot of attention. Its performance was compared with classical architectures such as ResNet18 and ResNet50. The results obtained showed that the ConvMixer structure gave very successful results and a very short operation time. Our proposed model has obtained an accuracy of 98.14%, 98.79%, and 97.69% for the ResNet18, ResNet50, and ConvMixer architectures, respectively. In addition, it has been observed that the processing time of the ConvMixer architecture is relatively short compared to these two architectures.

Heartbeat and Respiration Rate Prediction Using Combined Photoplethysmography and Ballisto Cardiography

Owing to the recent trends in remote health monitoring, real-time applications for measuring Heartbeat Rate and Respiration Rate (HARR) from video signals are growing rapidly. Photo Plethysmo Graphy (PPG) is a method that is operated by estimating the infinitesimal change in color of the human face, rigid motion of facial skin and head parts, etc. Ballisto Cardiography (BCG) is a nonsurgical tool for obtaining a graphical depiction of the human body’s heartbeat by inducing repetitive movements found in the heart pulses. The resilience against motion artifacts induced by luminance fluctuation and the patient’s mobility variation is the major difficulty faced while processing the real-time video signals. In this research, a video-based HARR measuring framework is proposed based on combined PPG and BCG. Here, the noise from the input video signals is removed by using an Adaptive Kalman filter (AKF). Three different algorithms are used for estimating the HARR from the noise-free input signals. Initially, the noise-free signals are subjected to Modified Adaptive Fourier Decomposition (MAFD) and then to Enhanced Hilbert vibration Decomposition (EHVD) and finally to Improved Variation mode Decomposition (IVMD) for attaining three various results of HARR. The obtained values are compared with each other and found that the EHVD is showing better results when compared with all the other methods.

Air-mattress system for ballistocardiogram-based heart rate and breathing rate estimation

Sleep-related problems are widespread. Numerous devices for sleep monitoring are increasingly available, including smartwatches, sleep monitoring rings, etc. These devices accumulate and analyze a substantial quantity of physiological data. In this study, we develop a smart air-mattress system that can effectively aid health measurements. The proposed system adopts an air-mattress system to detect subtle changes in pressure and thereby collect micro physiological signals, including ballistocardiography (BCG) and breathing signals. The system uses ultrasonic signals to detect the subject's turning movements. To increase the signal recognition accuracy, the BCG signal is processed effectively to reduce noise interference engendered by the body movement during sleep and is processed using regression analysis for heart rate and breathing rate estimation. Accordingly, the proposed system is unconstrained and can be used to collect micro BCG signals, breathing signals, heart rate signals, and turning movements for the long-term health-care.

Preservation of EEG spectral power features during simultaneous EEG-fMRI.

Electroencephalographic (EEG) data quality is severely compromised when recorded inside the magnetic resonance (MR) environment. Here we characterized the impact of the ballistocardiographic (BCG) artifact on resting-state EEG spectral properties and compared the effectiveness of seven common BCG correction methods to preserve EEG spectral features. We also assessed if these methods retained posterior alpha power reactivity to an eyes closure-opening (EC-EO) task and compared the results from EEG-informed fMRI analysis using different BCG correction approaches. Electroencephalographic data from 20 healthy young adults were recorded outside the MR environment and during simultaneous fMRI acquisition. The gradient artifact was effectively removed from EEG-fMRI acquisitions using Average Artifact Subtraction (AAS). The BCG artifact was corrected with seven methods: AAS, Optimal Basis Set (OBS), Independent Component Analysis (ICA), OBS followed by ICA, AAS followed by ICA, PROJIC-AAS and PROJIC-OBS. EEG signal preservation was assessed by comparing the spectral power of traditional frequency bands from the corrected rs-EEG-fMRI data with the data recorded outside the scanner. We then assessed the preservation of posterior alpha functional reactivity by computing the ratio between the EC and EO conditions during the EC-EO task. EEG-informed fMRI analysis of the EC-EO task was performed using alpha power-derived BOLD signal predictors obtained from the EEG signals corrected with different methods. The BCG artifact caused significant distortions (increased absolute power, altered relative power) across all frequency bands. Artifact residuals/signal losses were present after applying all correction methods. The EEG reactivity to the EC-EO task was better preserved with ICA-based correction approaches, particularly when using ICA feature extraction to isolate alpha power fluctuations, which allowed to accurately predict hemodynamic signal fluctuations during the EEG-informed fMRI analysis. Current software solutions for the BCG artifact problem offer limited efficiency to preserve the EEG spectral power properties using this particular EEG setup. The state-of-the-art approaches tested here can be further refined and should be combined with hardware implementations to better preserve EEG signal properties during simultaneous EEG-fMRI. Existing and novel BCG artifact correction methods should be validated by evaluating signal preservation of both ERPs and spontaneous EEG spectral power.

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.

Unobtrusive Cardio-Respiratory Assessment for Different Indoor Environmental Conditions

The achievement of a healthy indoor environ- ment—an indispensable requirement for human well-being—relies on the establishment of good air quality conditions and thermal comfort levels. This study addresses the development of a healthcare-focused Internet-of-Things (IoT) system for the cardiac assessment using photoplethysmography (PPG) and ballistocardiography (BCG) that can be applied in any indoor environment. Results from its experimental application include the analysis of heart rate variability (HRV) and respiratory status from a group of young adults under different thermal conditions and relative humidity (RH) levels. The final goal was to investigate the effects of short-term exposure to different air temperature and humidity conditions on human thermal comfort based on the analysis of the autonomic nervous system (ANS) activity. The experiment took place in a regular and nonisothermal office environment. Simulations based on computational fluid dynamics (CFD) were conducted to estimate heat distribution and select the optimal locations for the environmental sensor nodes’ placement. Results indicated that activation of the parasympathetic system was more notable in short-term exposures to environments with different air temperatures (24 °C–30 °C) than with different levels of RH (50%–70%). Changes in ambient air temperature led to the activation of thermal regulatory reflexes and thermal discomfort, perceived by an increase in the ratio of low frequency to high frequency (LF/HF) at higher air temperature exposures. In addition, HRV parameters were used to train machine learning models on predicting if the user is in a thermally comfortable environment or a discomfortable environment (hot or humid), having achieved an 87% average accuracy.

A Conv -Transformer network for heart rate estimation using ballistocardiographic signals

ObjectiveContinuous heart rate (HR) monitoring has great implications for the prevention of chronic diseases, and we use non-contact ballistocardiography(BCG) technology to estimate HR. MethodsIn this paper, overnight BCG data are acquired from 78 patients using a 10-channel piezoelectric sensor matrix with a sampling rate of 50 Hz. 200 sets of non-overlapping 10 s quiet period data are selected for follow-up work, with a total of 15,600 segments. A Conv-Transformer network with Pyramid input (HRCTP-net) is used to estimate HR values for these segments, where local features are obtained by CNN and global features are calculated by the transformer. This is an end-to-end network without additional post-processing. During the experiment, electrocardiogram (ECG) noise which from the MIT-BIH noise stress test database is also introduced for data augmentation to further improve the network generalization ability. ResultsTaking the synchronously collected ECG as the ground truth, the results in the 6-fold cross-validation show that the proposed method achieves the best results on mean absolute error (MAE), standard deviation of absolute error (SDAE) and pearson correlation coefficient (PCC) with 0.93 bpm, 1.31 bpm and 0.97, respectively. ConclusionTo the best of our knowledge, this paper is the first time to introduce transformer and ECG noise into BCG signal analysis and demonstrate their effectiveness. SignificanceOur proposed HRCTP-net has potential and promise in healthcare applications.

A support system for automatic classification of hypertension using BCG signals

Hypertension (HPT) is a lethal medical disorder in which the blood vessels unusually have high pressure for an extended period. It may raise the risk of various health complications. Ballistocardiography (BCG) is an emerging tool to diagnose various heart-related diseases and is used to depict the repetitive vibrations in the human body induced by the sudden evacuation of blood into the major arteries with each heartbeat. This article presents the multi-resolution analysis of BCG signals for the screening of HPT patients using integrated tunable Q-factor wavelet transform (ITQWT). The TQWT decomposes an input BCG signal into various sub-bands (SBs). Specifying a predetermined accurate basis function for optimal decomposition utilizing TQWT is a difficult task. Therefore, in this study, for the first time, a multi-verse optimization (MVO) algorithm is integrated with TQWT for selecting optimum tuning parameters to decompose the input BCG signals into more representative SBs. To detect hypertensive BCG signals eleven statistical features are evaluated from each SB. Among them, a set of seven statistically-significant features are selected by applying the Kruskal–Wallis test and fed to a K-nearest neighbor (K-NN) classifier with six different kernels using a 10-fold validation scheme. The highest classification accuracy of 92.21%, sensitivity of 92.96%, and specificity of 91.60% are achieved using a weighted K-NN classifier. This paper presents, a non-parameterized approach for the optimal decomposition of BCG data to detect HPT more accurately. The primary benefit of the proposed support system is that it can detect HPT patients with high accuracy by reducing the clinician’s workload.

BALLISTOCARDIOGRAPHY TO CHARACTERIZE PULMONARY ARTERY PRESSURE IN ADVANCED HEART FAILURE PATIENTS AND HEALTHY ADULTS

Remote monitoring to manage heart failure (HF) at home can reduce readmission rates. Notably the implantable CardioMEMS device that measures pulmonary artery pressure (PAP) has proven effective but is both invasive and costly. Our goal is to develop smart home sensors that can monitor HF patients non-invasively and without requiring active patient engagement. The objective of this study is the development and evaluation of ballistocardiography (BCG) as a candidate technology for non-invasive, autonomous monitoring of PAP for HF patients.

OCSTN: One-class time-series classification approach using a signal transformation network into a goal signal

One-class classification (OCC) is a classification task where the training data have only one class. The goal is to classify input data into one seen class or other unseen classes. This paper proposes an OCC approach using a signal transformation network (OCSTN), which aims to process univariate time-series data. The main contribution is developing a signal transformation network (STN) that aims to transform input signals into one signal, namely the goal signal. Moreover, the model error of the STN is a distance metric between the goal signal and the model output. The STN model learns from one-class signals. Therefore, model error for one class is small relative to other classes. Accordingly, OCSTN could discriminate between seen and unseen classes using the model errors. The proposed OCSTN is evaluated using two ballistocardiography (BCG) datasets. The OCSTN achieves fair results in both AUC scores and processing speed. OCSTN has a weak point in training diverse signals. In addition, the entropy and smoothness of the goal signal are highly related to the AUC score.

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.

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.

I Want to Know Your Hand

We present HoldPass, the first system that can authenticate a user while they simply hold their phone. It uses the heart activity as biometric trait sensed via the hand vibrations in response to the cardiac cycle - a process known as ballistocardiography (BCG). While heart activity has been used for biometric authentication, sensing it through hand-based ballistocardiography (Hand-BCG) using standard sensors found on commodity mobile phones is an uncharted territory. Using a combination of in-depth qualitative analysis and large-scale quantitative analysis involving over 100 volunteers, we paint a detailed picture of opportunities and challenges. Authentication based on Hand-BCG is shown to be feasible but the signal is weak, uniquely prone to motion artifacts and does not land itself to the common approach of alignment-based authentication. HoldPass addresses these challenges by introducing a novel alignment-free authentication scheme that builds on asynchronous signal slicing and a data-driven algorithm for identifying a reduced set of features for characterizing a user. We implement HoldPass and evaluate it using a multi-modal approach: a large-case study involving 112 volunteers and targeted studies with a smaller set of volunteers over a period of several months. The data shows that HoldPass provides an authentication accuracy and user experience on par with or better than state-of-the-art systems with stronger requirements on hardware and/or user participation.

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.

Automated and precise heartbeat detection in ballistocardiography signals using bidirectional LSTM

Ballistocardiography (BCG) provides an unobtrusive, mechanical measurement of heartbeat for early detection and longitudinal monitoring of cardiovascular conditions, although it is plagued by poor signal to noise ratio. Despite tremendous efforts over the last decade in related advanced signal processing and deep learning research, automated and precise heartbeat detection in BCG remains a major technical challenge. In this work, we design and study a sequential deep neural network based approach that addresses three key issues in automated BCG learning and detection: auto-labeling of BCG samples in the raw training data; design of a recursive neural network that can learn the association between the continuous BCG waves and the label sequence in the form of sample-by-sample predictions; and finally, heartbeat detection from the output sequence. We evaluate the proposed method using our data set comprising 8 human subjects under varying cardiac output conditions: pre-exercise and post-exercise. Using multiple performance metrics, we report that the proposed method can achieve a heart rate accuracy of 98.68% and root-mean-square-error of 1.37 bpm with 98.85% coverage, which compare favorably to two state-of-the-art methods in the same test.