Ballistocardiogram Signals Research Articles

A ballistocardiogram dataset with reference sensor signals in long-term natural sleep environments

To facilitate unobtrusive and continuous sleep monitoring and promote intelligent sleep quality assessment, we present a dataset that includes multiple nights of continuous ballistocardiogram (BCG) data collected using piezoelectric film sensors from 32 subjects in their regular sleep environments. Besides, the referenced heart rate and respiratory data are also recorded by reference sensors to validate the accuracy of the cardiac and respiratory components extracted from the BCG signals. The dataset serves as a foundation for research on unobtrusive vital sign monitoring based on BCG signals, offering data support for the evaluation and optimization of sleep quality.

A deep learning method for contactless emotion recognition from ballistocardiogram

Emotion recognition is a major research point in the field of affective computing. Existing research on the application of physiological signals to emotion recognition mainly focuses on the processing of contact signals. However, there are issues with contact signal acquisition equipment, such as limited portability and poor user compliance, which make it difficult to promote its use. To explore a new method for emotion recognition based on contactless ballistocardiogram (BCG), we proposed a SE-CNN model with a multi-class focal loss function. To construct the dataset, we used audio-visual stimuli to evoke the subjects’ emotions and collected data on the subjects’ three discrete emotions, positive, neutral, and negative, through our established BCG signal acquisition system based on a piezoelectric ceramics sensor. Root mean square filter and thresholding were used to detect and eliminate motion artifacts of BCG signals. We did two kinds of preprocessing on BCG signals: wavelet transform and bandpass filtering, to explore the effect of different components of BCG on emotion recognition. Subsequently, we verified the model’s performance and cross-time working ability through traditional K-Fold and our proposed K-Session cross-validation methods. The results showed that the band-pass filtering method was more beneficial to the current classification task. Under K-Fold cross-validation, the model’s accuracy, precision, and recall were 97.21%, 97.00%, and 97.11%. Under K-Session cross-validation, the model’s accuracy, precision, and recall were 94.66%, 93.92%, and 94.86%, respectively, all of which were better than the classification effect of synchronous ECG. The reliability of BCG in contactless emotion recognition was proved.

Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation

BackgroundHeart rate, as the four vital signs of human body, is a basic indicator to measure a person's health status. Traditional electrocardiography (ECG) measurement, which is routinely monitored, requires subjects to wear lead electrodes frequently, which undoubtedly places great restrictions on participants' activities during the normal test. At present, the boom of wearable devices has created hope for non-invasive, simple operation and low-cost daily heart rate monitoring, among them, Ballistocardiogram signal (BCG) is an effective heart rate measurement method, but in the actual acquisition process, the robustness of non-invasive vital sign collection is limited. Therefore, it is necessary to develop a method to improve the robustness of heart rate monitoring. ObjectiveTherefore, in view of the problem that the accuracy of untethered monitoring heart rate is not high, we propose a method aimed at detecting the heartbeat cycle based on BCG to accurately obtain the beat-to-beat heart rate in the sleep state. MethodsIn this study, we implement an innovative J-wave detection algorithm based on BCG signals. By collecting BCG signals recorded by 28 healthy subjects in different sleeping positions, after preprocessing, the data feature set is formed according to the clustering of morphological features in the heartbeat interval. Finally, a J-wave recognition model is constructed based on bi-directional long short-term memory (BiLSTM), and then the number of J-waves in the input sequence is counted to realize real-time detection of heartbeat. The performance of the proposed heartbeat detection scheme is cross-verified, and the proposed method is compared with the previous wearable device algorithm. ResultsThe accuracy of J wave recognition in BCG signal is 99.67%, and the deviation rate of heart rate detection is only 0.27%, which has higher accuracy than previous wearable device algorithms. To assess consistency between method results and heart rates obtained by the ECG, seven subjects are compared using Bland-Altman plots, which show no significant difference between BCG and ECG results for heartbeat cycles. ConclusionsCompared with other studies, the proposed method is more accurate in J-wave recognition, which improves the accuracy and generalization ability of BCG-based continuous heartbeat cycle extraction, and provides preliminary support for wearable-based untethered daily monitoring.

Interpretable synthetic signals for explainable one-class time-series classification

This research paper introduces an innovative approach for explainable one-class time-series classification (XOCTSC). The proposed method involves generating pseudounseen synthetic signals by altering the amplitude and cycle of the original signals. Subsequently, a classification process is performed to distinguish between the original and synthetic signals, and the resulting model is applied to testing data. Instances classified as synthetic classes are treated as unseen classes, and the dissimilarity with the training data can be elucidated through an explanation of the synthetic class creation process. This approach aims to enhance the interpretability of one-class time-series classification models by providing insights into the reasoning behind their decisions. The proposed method is demonstrated with a ballistocardiogram (BCG) signal for the breathing dataset and an electroencephalogram (EEG) signal for the epilepsy dataset. The proposed method recognizes BCG amplitude reduction during breath holding. Moreover, EEG cycle changes during epileptic seizures are observed across multiple channels. These observations align with actual epilepsy symptoms and breathing behaviors.

Improving ballistocardiogram-based continuous heart rate variability monitoring: A self-supervised learning approach

Heart rate variability (HRV) is a reliable measure of an individual’s physical and mental fitness. Monitoring HRV over time provides crucial indicators of various health issues and helps in the early implementation of preventive measures. While the ballistocardiogram (BCG) serves as an alternative to the electrocardiogram (ECG) for convenient at-home HRV monitoring, it is inevitable to face the presence of undesirable artifacts and irregular waveforms in BCG signals that can hinder accurate monitoring when appropriate interventions are unavailable. To address this challenge, we propose a self-supervised learning approach consisting of three stages: signal separation, heartbeat pattern detection, and peak identification. We introduce convolutional kernels of different sizes to construct adaptive soft filters that effectively extract the heartbeat waveform from the original signal. To discover the heartbeat patterns, we utilize the k-means algorithm for clustering the encoded peaks obtained from the masked autoencoder and then compute Kullback–Leibler divergences to determine the optimal number of clusters. Subsequently, a lightweight multi-feature cross-attention classifier is employed to locate the heartbeats. Although the classifier is trained using pseudo-labels generated in the previous stage, the inherent similarity of heartbeats can enhance its performance. Our approach accommodates diverse BCG signal modes and does not require an annotated dataset for model training. Through evaluations conducted on 24 healthy subjects using synchronized ECG references, our prototype system demonstrated an accuracy of approximately 99% in heartbeat identification and an interbeat interval RMSE of under 15ms. These results suggest the system’s practicality and suitability for real-world HRV monitoring.

Cardiovascular sex-differences: insights via physiology-based modeling and potential for noninvasive sensing via ballistocardiography.

In this study, anatomical and functional differences between men and women in their cardiovascular systems and how these differences manifest in blood circulation are theoretically and experimentally investigated. A validated mathematical model of the cardiovascular system is used as a virtual laboratory to simulate and compare multiple scenarios where parameters associated with sex differences are varied. Cardiovascular model parameters related with women's faster heart rate, stronger ventricular contractility, and smaller blood vessels are used as inputs to quantify the impact (i) on the distribution of blood volume through the cardiovascular system, (ii) on the cardiovascular indexes describing the coupling between ventricles and arteries, and (iii) on the ballistocardiogram (BCG) signal. The model-predicted outputs are found to be consistent with published clinical data. Model simulations suggest that the balance between the contractile function of the left ventricle and the load opposed by the arterial circulation attains similar levels in females and males, but is achieved through different combinations of factors. Additionally, we examine the potential of using the BCG waveform, which is directly related to cardiovascular volumes, as a noninvasive method for monitoring cardiovascular function. Our findings provide valuable insights into the underlying mechanisms of cardiovascular sex differences and may help facilitate the development of effective noninvasive cardiovascular monitoring methods for early diagnosis and prevention of cardiovascular disease in both women and men.

Extraction of Heartbeat Feature Based on Ballistocardiogram Signal From Multichannel Piezoelectric Ceramic Sensors

Extraction of Heartbeat Feature Based on Ballistocardiogram Signal From Multichannel Piezoelectric Ceramic Sensors

Noncontact Monitoring of Heart Rate Variability Using a Fiber Optic Sensor

Heart rate variability (HRV) is widely investigated to provide early warning signs for cardiovascular diseases (CVDs). However, traditional HRV monitoring methods are inconvenient in daily long-term continuous monitoring due to the uncomfortable direct contact of the devices with the skin. In this work, we present a ballistocardiogram (BCG)-based system for long-term HRV monitoring in a non-contact mode. BCG signals can reflect the mechanical activity of the heart, from which the beat-to-beat interval (BBI) can be extracted for HRV analysis. An effective solution is demonstrated for accurate and steady BCG signal detection using a highly-sensitive fiber optic sensor. A robust heartbeat extraction algorithm based on the deep learning method is proposed to improve the accuracy of the BBI extraction. Continuous wavelet transform (CWT) is introduced to facilitate feature extraction against the variation of morphological characteristics of BCG signal. The accuracy of BBI estimation presents a median error of 4.4 ms, indicating the effectiveness and feasibility of the proposed system. This work is expected to pave a new and practical pathway for household HRV monitoring during sleep.

Non-invasive monitoring of cardiac function through Ballistocardiogram: an algorithm integrating short-time Fourier transform and ensemble empirical mode decomposition.

The Ballistocardiogram (BCG) is a vibration signal that is generated by the displacement of the entire body due to the injection of blood during each heartbeat. It has been extensively utilized to monitor heart rate. The morphological features of the BCG signal serve as effective indicators for the identification of atrial fibrillation and heart failure, holding great significance for BCG signal analysis. The IJK-complex identification allows for the estimation of inter-beat intervals (IBI) and enables a more detailed analysis of BCG amplitude and interval waves. This study presents a novel algorithm for identifying the IJK-complex in BCG signals, which is an improvement over most existing algorithms that only perform IBI estimation. The proposed algorithm employs a short-time Fourier transform and summation across frequencies to initially estimate the occurrence of the J wave using peak finding, followed by Ensemble Empirical Mode Decomposition and a regional search to precisely identify the J wave. The algorithm's ability to detect the morphological features of BCG signals and estimate heart rates was validated through experiments conducted on 10 healthy subjects and 2 patients with coronary heart disease. In comparison to commonly used methods, the presented scheme ensures accurate heart rate estimation and exhibits superior capability in detecting BCG morphological features. This advancement holds significant value for future applications involving BCG signals.

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation.

Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Paving a path of technological innovation in the clinical field, a very active line of research is currently dealing with the possibility to rely on non-medical-graded low-cost devices, to develop unattended telemedicine (TM) solutions aimed at non-invasively gathering data, signals, and images. In this article, a TM solution is proposed for monitoring the heart rate (HR) of patients during sleep. A remote patient monitoring system (RPMS) featuring a smart belt equipped with pressure sensors for ballistocardiogram (BCG) signals sampling was deployed. A field trial was then conducted over a 2-month period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a photoplethysmography (PPG) signal as the gold standard, to examine the feasibility of the solution via the estimation of HR values from the collected BCG signals. For this purpose, two of the highest-performing approaches for HR estimation from BCG signals, one algorithmic and the other based on a convolutional neural network (CNN), were retrieved from the literature and updated for a TM-related use case. Finally, HR estimation performances were assessed in terms of patient-wise mean absolute error (MAE). Results retrieved from the literature (controlled environment) outperformed those achieved in the experimentation (TM environment) by 29% (MAE = 4.24 vs. 5.46, algorithmic approach) and 52% (MAE = 2.32 vs. 3.54, CNN-based approach), respectively. Nonetheless, a low packet loss ratio, restrained elaboration time of the collected biomedical big data, low-cost deployment, and positive feedback from the users, demonstrate the robustness, reliability, and applicability of the proposed TM solution. In light of this, further steps will be planned to fulfill new targets, such as evaluation of respiratory rate (RR), and pattern assessment of the movement of the participants overnight.

Unconstrained blood pressure monitoring based on a neural network–assisted multistage pressure textile sensor

Non-invasive blood pressure (NIBP) assessment via constrained wearable devices aided by intelligent algorithms has led to tremendous advancements in healthcare. However, existing NIBP assessment methods generally impose hard constraints on human behavior, resulting in lower accuracy of blood pressure assessments. Therefore, for unconstrained ambulatory blood pressure monitoring, we propose a textile-based sensor with a multistage structure that acquires human ballistocardiogram (BCG) signals. Using nylon fabric as a substrate, a multistage structure consisting of sensitive micro-cilia and nylon supports ensures that the sensor maintains high sensitivity (0.533 V/kPa) even under static pressure (0–7 kPa) equal to the self-weight of the human body. The BCG waves acquired by the sensor have excellent agreement with the cardiac vibration wave obtained with laser vibrometry (displacement precision of 0.5 pm). Benefiting from the accurate acquisition of BCG, the time interval between each characteristic point of BCG waves is independently used as the characteristic parameter. A blood pressure assessment model via a back-propagation neural network is proposed to realize an unconstrained blood pressure assessment. For unconstrained measurements of diastolic pressure (DP) and systolic pressure (SP), the mean absolute error and standard deviation of error were 3.90 ± 4.79 mmHg and 4.62 ± 6.00 mmHg, respectively; this result meets the accuracy criteria of the American Association for the Advancement of Medical Instrumentation for Class II medical. We propose a new approach for unconstrained ambulatory blood pressure monitoring that shows great potential in home, hospital, and personalized medicine.

Monitoring of Cardiorespiratory Parameters during Sleep Using a Special Holder for the Accelerometer Sensor.

Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and accurately measure cardiorespiratory parameters, is of great interest. The aim of this study is to validate a non-invasive and unobtrusive cardiorespiratory parameter monitoring system based on an accelerometer sensor. This system includes a special holder to install the system under the bed mattress. The additional aim is to determine the optimum relative system position (in relation to the subject) at which the most accurate and precise values of measured parameters could be achieved. The data were collected from 23 subjects (13 males and 10 females). The obtained ballistocardiogram signal was sequentially processed using a sixth-order Butterworth bandpass filter and a moving average filter. As a result, an average error (compared to reference values) of 2.24 beats per minute for heart rate and 1.52 breaths per minute for respiratory rate was achieved, regardless of the subject's sleep position. For males and females, the errors were 2.28 bpm and 2.19 bpm for heart rate and 1.41 rpm and 1.30 rpm for respiratory rate. We determined that placing the sensor and system at chest level is the preferred configuration for cardiorespiratory measurement. Further studies of the system's performance in larger groups of subjects are required, despite the promising results of the current tests in healthy subjects.

Wrist ballistocardiography and invasively recorded blood pressure in healthy volunteers during reclining bike exercise.

Objective: Ballistocardiogram (BCG) features are of interest in wearable cardiovascular monitoring of cardiac performance. We assess feasibility of wrist acceleration BCG during exercise for estimating pulse transit time (PTT), enabling broader cardiovascular response studies during acute exercise and improved monitoring in individuals at risk for cardiovascular disease (CVD). We also examine the relationship between PTT, blood pressure (BP), and stroke volume (SV) during exercise and posture interventions. Methods: 25 participants underwent a bike exercise protocol with four incremental workloads (0W, 50W, 100W, and 150W) in supine and semirecumbent postures. BCG, invasive radial artery BP, tonometry, photoplethysmography (PPG) and echocardiography were recorded. Ensemble averages of BCG signals determined aortic valve opening (AVO) timings, combined with peripheral pulse wave arrival times to calculate PTT. We tested for significance using Wilcoxon signed-rank test. Results: BCG was successfully recorded at the wrist during exercise. PTT exhibited a moderate negative correlation with systolic BP (ρSup = -0.65, ρSR = -0.57, ρAll = -0.54). PTT differences between supine and semirecumbent conditions were significant at 0W and 50W (p < 0.001), less at 100W (p = 0.0135) and 150W (p = 0.031). SBP and DBP were lower in semirecumbent posture (p < 0.01), while HR was slightly higher. Echocardiography confirmed association of BCG features with AVO and indicated a positive relationship between BCG amplitude and SV (ρ = 0.74). Significance: Wrist BCG may allow convenient PTT and possibly SV tracking during exercise, enabling studies of cardiovascular response to acute exercise and convenient monitoring of cardiovascular performance.

What filter passband should be applied to the ballistocardiogram?

The ballistocardiogram (BCG) is receiving an increasing interest for ubiquitous cardiovascular health and disease monitoring. Yet, prior effort to analyze its harmonic and frequency contents is limited. In-depth understanding of the harmonic and frequency structure of the BCG has the potential to facilitate the development of various BCG-based cardiovascular health monitoring methods. In particular, when filtering the BCG, an optimal passband derived based on the knowledge of the harmonic and frequency structure of the BCG can reconcile signal integrity and noise robustness. Therefore, we conducted a comprehensive harmonic and frequency analysis using the head-to-foot BCG collected from 22 volunteers to quantify the importance of each harmonic component in the BCG and investigate the effect of filtering passband on its signal morphology and waves. Overall, the 2nd to the 5th harmonics accounted for most of the total variance in the BCG (14.5 ± 13%, 36.3 ± 18.7%, 21.6 ± 14%, and 14.6 ± 11.3% for the 2nd to the 5th harmonics). The frequency of the important harmonics (percentage variance ≥ 10%) ranged from 2.9 Hz to 7.8 Hz. When filtering the BCG, the widely used ≤ 1 Hz lower bound of the passband may be increased to 3.5 Hz and 1.5 Hz without causing substantial alterations to the BCG timings and amplitudes, respectively. Likewise, a 15 Hz and 20 Hz upper bound of the passband appears to be relevant, respectively. The insights garnered in this work may guide future development of the BCG-based cardiovascular health monitoring methods by application-specific tailoring of the frequency band associated with the BCG signal filtering.

Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study.

Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user's sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.

Mattress-Based Non-Influencing Sleep Apnea Monitoring System.

A mattress-type non-influencing sleep apnea monitoring system was designed to detect sleep apnea-hypopnea syndrome (SAHS). The pressure signals generated during sleep on the mattress were collected, and ballistocardiogram (BCG) and respiratory signals were extracted from the original signals. In the experiment, wavelet transform (WT) was used to reduce noise and decompose and reconstruct the signal to eliminate the influence of interference noise, which can directly and accurately separate the BCG signal and respiratory signal. In feature extraction, based on the five features commonly used in SAHS, an innovative respiratory waveform similarity feature was proposed in this work for the first time. In the SAHS detection, the binomial logistic regression was used to determine the sleep apnea symptoms in the signal segment. Simulation and experimental results showed that the device, algorithm, and system designed in this work were effective methods to detect, diagnose, and assist the diagnosis of SAHS.

NF-Heart

The increasingly remote workforce resulting from the global coronavirus pandemic has caused unprecedented cybersecurity concerns to organizations. Considerable evidence has shown that one-pass authentication fails to meet security needs when the workforce work from home. The recent advent of continuous authentication (CA) has shown the potential to solve this predicament. In this paper, we propose NF-Heart, a physiological-based CA system utilizing a ballistocardiogram (BCG). The key insight is that the BCG measures the body's micro-movements produced by the recoil force of the body in reaction to the cardiac ejection of blood, and we can infer cardiac biometrics from BCG signals. To measure BCG, we deploy a lightweight accelerometer on an office chair, turning the common chair into a smart continuous identity "scanner". We design multiple stages of signal processing to decompose and transform the distorted BCG signals so that the effects of motion artifacts and dynamic variations are eliminated. User-specific fiducial features are then extracted from the processed BCG signals for authentication. We conduct comprehensive experiments on 105 subjects in terms of verification accuracy, security, robustness, and long-term availability. The results demonstrate that NF-Heart achieves a mean balanced accuracy of 96.45% and a median equal error rate of 3.83% for CA. The proposed signal processing pipeline is effective in addressing various practical disturbances.

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

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

Comparison of Ballistocardiogram Processing Methods Based on Fiber Specklegram Sensors

The ballistocardiogram (BCG) is a graphic representation of the movements of the body associated with cardiac activity. In this article, a 10-min BCG has been captured for ten different volunteers with a polymer optical fiber (POF) specklegram sensor. This transducer, which is composed of a charge-coupled device (CCD) camera, a laser emitting diode, and two meters of POF, allows capturing the BCG by analyzing how the induced speckle pattern changes over time. These changes are related to cardiac activity. Several processing methods have been compared to determine which method achieves the best performance: complex cepstrum, power of spectral density (PSD), Pam–Tompkins algorithm, wavelet, autocorrelation, Savitzky–Golay filter, mean absolute deviation, and Hilbert transform. Accuracy and resource consumption have been characterized and compared for these methods. Hilbert, PSD, and Savitzky–Golay exhibit both small errors and computational costs. This article describes a baseline for the main frequency determination of POF-based BCG signals in real-time.

Atrial Fibrillation Detection Based on a Residual CNN Using BCG Signals

Atrial fibrillation (AF) is the most common arrhythmia and can seriously threaten patient health. Research on AF detection carries important clinical significance. This manuscript proposes an AF detection method based on ballistocardiogram (BCG) signals collected by a noncontact sensor. We first constructed a BCG signal dataset consisting of 28,214 ten-second nonoverlapping segments collected from 45 inpatients during overnight sleep, including 9438 for AF, 9570 for sinus rhythm (SR), and 9206 for motion artifacts (MA). Then, we designed a residual convolutional neural network (CNN) for AF detection. The network has four modules, namely a downsampling convolutional module, a local feature learning module, a global feature learning module, and a classification module, and it extracts local and global features from BCG signals for AF detection. The model achieved precision, sensitivity, specificity, F1 score, and accuracy of 96.8%, 93.7%, 98.4%, 95.2%, and 96.8%, respectively. The results indicate that the AF detection method proposed in this manuscript could serve as a basis for long-term screening of AF at home based on BCG signal acquisition.