Article

Camera-Based Seismocardiogram for Heart Rate Variability Monitoring

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Abstract

Heart rate variability (HRV) is a crucial metric that quantifies the variation between consecutive heartbeats, serving as a significant indicator of autonomic nervous system (ANS) activity. It has found widespread applications in clinical diagnosis, treatment, and prevention of cardiovascular diseases. In this study, we proposed an optical model for defocused speckle imaging, to simultaneously incorporate out-of-plane translation and rotation-induced motion for highly-sensitive non-contact seismocardiogram (SCG) measurement. Using electrocardiogram (ECG) signals as the gold standard, we evaluated the performance of photoplethysmogram (PPG) signals and speckle-based SCG signals in assessing HRV. The results indicated that the HRV parameters measured from SCG signals extracted from laser speckle videos showed higher consistency with the results obtained from the ECG signals compared to PPG signals. Additionally, we confirmed that even when clothing obstructed the measurement site, the efficacy of SCG signals extracted from the motion of laser speckle patterns persisted in assessing the HRV levels. This demonstrates the robustness of camera-based non-contact SCG in monitoring HRV, highlighting its potential as a reliable, non-contact alternative to traditional contact-PPG sensors.

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... The advantages of SV over the aforementioned methods are its insensitivity to background noise and independence of visible skin. The feasibility of using SV to monitor average heart rate [18][19][20], instantaneous heart rate [21,22], and average respiration rate [19] has been described in existing literature, including our previous work [18,21]. However, there has been an absence of clinical studies on the implementation of SV in a real-life clinical setting. ...
... The performance of M(t) on the chest is comparable to the performance (an LOA of [−23.41, 25.45] ms) reported in [22] that used SV on the chest of healthy volunteers in a laboratory setting. Furthermore, it exceeds the performance (an SD of 25.2 ms) reported in [16] using SPG on healthy volunteers in a laboratory setting. ...
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... The study protocol was approved by the Westphalia-Lippe Ethics Committee (act ref. l 2024-134-f-S, 19 March 2024). ...
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Physiological variation of the interval between consecutive heartbeats is known as the heart rate variability (HRV). HRV analysis is traditionally performed on electrocardiograms (ECG signals) and has become a useful tool in the diagnosis of different clinical and functional conditions. The progress in the sensor technique encouraged the development of alternative methods of analyzing cardiac activity: Seismocardiography and gyrocardiography. In our study we performed HRV analysis on ECG, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) using the PhysioNet Cardiovascular Toolbox. The heartbeats in ECG were detected using the Pan-Tompkins algorithm and the heartbeats in SCG and GCG signals were detected as peaks within 100 ms from the occurrence of the ECG R waves. The results of time domain, frequency domain and nonlinear HRV analysis on ECG, SCG and GCG signals are similar and this phenomenon is confirmed by very strong linear correlation of HRV indices. The differences between HRV indices obtained on ECG and SCG and on ECG and GCG were statistically insignificant and encourage using SCG or GCG for HRV estimation. Our results of HRV analysis confirm stronger correlation of HRV indices computed on ECG and GCG signals than on ECG and SCG signals because of greater tolerance to inter-subject variability and disturbances.
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Introduction: Heart Rate Variability (HRV) and Pulse Rate Variability (PRV), are non-invasive techniques for monitoring changes in the cardiac cycle. Both techniques have been used for assessing the autonomic activity. Although highly correlated in healthy subjects, differences in HRV and PRV have been observed under various physiological conditions. The reasons for their disparities in assessing the degree of autonomic activity remains unknown. Methods: To investigate the differences between HRV and PRV, a whole-body cold exposure (CE) study was conducted on 20 healthy volunteers (11 male and 9 female, 30.3 ± 10.4 years old), where PRV indices were measured from red photoplethysmography signals acquired from central (ear canal, ear lobe) and peripheral sites (finger and toe), and HRV indices from the ECG signal. PRV and HRV indices were used to assess the effects of CE upon the autonomic control in peripheral and core vasculature, and on the relationship between HRV and PRV. The hypotheses underlying the experiment were that PRV from central vasculature is less affected by CE than PRV from the peripheries, and that PRV from peripheral and central vasculature differ with HRV to a different extent, especially during CE. Results: Most of the PRV time-domain and Poincaré plot indices increased during cold exposure. Frequency-domain parameters also showed differences except for relative-power frequency-domain parameters, which remained unchanged. HRV-derived parameters showed a similar behavior but were less affected than PRV. When PRV and HRV parameters were compared, time-domain, absolute-power frequency-domain, and non-linear indices showed differences among stages from most of the locations. Bland-Altman analysis showed that the relationship between HRV and PRV was affected by CE, and that it recovered faster in the core vasculature after CE. Conclusion: PRV responds to cold exposure differently to HRV, especially in peripheral sites such as the finger and the toe, and may have different information not available in HRV due to its non-localized nature. Hence, multi-site PRV shows promise for assessing the autonomic activity on different body locations and under different circumstances, which could allow for further understanding of the localized responses of the autonomic nervous system.
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A framework to detect aortic valve opening (AO) phase with the help of seismocardiogram (SCG) signal is proposed. A small electronic circuit board is designed, which consists of 3-D MEMS based accelerometer, pre-amplifier, and filter. It is interfaced with standard data acquisition system to record SCG signals. The signal is decomposed using a proposed modified variational mode decomposition technique. In the first stage of decomposition, baseline drift is suppressed. Whereas, in the second stage, signal information related to AO instants are extracted. Gaussian derivative filtering is performed on each of the decomposed modes to enhance the systolic profiles. These filtered modes are named as Gaussian derivative filtered modes (GDFMs). The GDFMs with probable AO peaks are selected based on proposed relative GDFM energy (RGE). The signal is reconstructed from the selected GDFMs and it is emphasized using the weights derived from squared RGE. The iteratively extracted maximum slope information is incorporated for systole envelope construction. Finally, peaks are detected using Hilbert transform and cardiac cycle envelope. The robustness of the proposed framework is evaluated using clean and noisy SCG signals from two different databases. For publicly available database (CEBS, Physionet), mean detection error rate 5.2%, sensitivity 97.3%, positive predictivity 97.4%, and detection accuracy 95.1% are found. For our real-time SCG database, the values of these metrics are 6.9%, 96.7%, 96.4%, and 93.4%, respectively. The developed system shows good detection rates even on less number of analyzed beats.
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Background: Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals. Methods: We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features. Results: Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats ([Formula: see text], [Formula: see text]) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination ([Formula: see text]) to determine goodness of fit to linear model. The highest [Formula: see text] values were obtained for mean interbeat interval ([Formula: see text] for reference algorithm, [Formula: see text] in the worst case), [Formula: see text] and [Formula: see text] ([Formula: see text] for the best case, [Formula: see text] for the worst case) and the lowest were obtained for [Formula: see text] ([Formula: see text] in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the [Formula: see text] values of pNN50 values in signals p001-p020 and for all analyzed signals. Conclusions: Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and [Formula: see text]. Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal.
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Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG.
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In the past decade, there has been a resurgence in the field of unobtrusive cardiomechanical assessment, through advancing methods for measuring and interpreting ballistocardiogram (BCG) and seismocardiogram (SCG) signals. Novel instrumentation solutions have enabled BCG and SCG measurement outside of clinical settings: in the home, in the field, and even in microgravity. Customized signal processing algorithms have led to reduced measurement noise, clinically relevant feature extraction, and signal modeling. Finally, human subjects physiology studies have been conducted using these novel instruments and signal processing tools with promising clinically relevant results. This paper reviews the recent advances in these areas of modern BCG and SCG research.
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We present a new method for automatic detection of peaks in noisy periodic and quasi-periodic signals. The new method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima. The usefulness of the proposed method is shown by applying the AMPD algorithm to simulated and real-world signals.
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Heart rate variability (HRV) is a valuable noninvasive tool of assessing the state of cardiovascular autonomic function. Over the recent years there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing cardiovascular vibrations. The purpose of this study is to compare HRV indices calculated on SCG and ECG signals from Combined measurement of ECG, breathing and seismocardiogram (CEBS) database. The authors use 20 signals lasting 200 s acquired from patients in supine position and compare heart rate variability parameters from the seismocardiogram and ECG reference signal. They assessed the performance of heart beat detector on SCG channel. The results of modified version of SCG heart beat detection prove its good performance on signals with higher sampling frequency. Strong linear correlation of HRV indices calculated from ECG and SCG prove the reliability of SCG in HRV analysis performed on signals from CEBS Database.
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The seismocardiogram (SCG) signal is an effective representation of the heart contraction and blood ejection behavior for human subjects and exhibits in the vibrations of chest wall surface. The noncontact sensing of cardiac information using a microwave Doppler radar has the potential to monitor SCG without attaching sensors to the body. In this paper, the similarity of the radar acceleration waveforms (RAWs) to SCG is investigated in terms of the waveform morphology and fiducial points within a frequency band of 18-35 Hz, wherein the RAWs are less influenced by low frequency interference. A high morphological similarity is demonstrated by a high cross correlation coefficient between RAW and the dorso-ventral SCG (SCGz) waveform, including a coefficient over 0.9 for 5 of 8 subjects and a minimum value of 0.72 for one subject experiencing interference. For the fiducial points, RAWs can correctly provide the locations of aortic valve opening (AO), an important fiducial point, with a root mean square deviation from the SCGz AO locations less than 2 ms for 6 of 8 subjects. In addition, experiments are set up to evaluate the time shift of AO points before and after a 90-second exercise. The results show that RAWs can correctly determine the time shift directions of AO for all subjects, and estimate the decrease of pre-ejection period (PEP) with an accuracy of less than 2 ms of that derived from SCG for 6 of 8 subjects. These investigation results demonstrate the effectiveness of using the noncontact Doppler radar for SCG measurements.
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The electrocardiogram (ECG) signal basically corresponds to the electrical activity of the heart. In the literature, the ECG signal has been analyzed and utilized for various purposes, such as measuring the heart rate, examining the rhythm of heartbeats, diagnosing heart abnormalities, emotion recognition and biometric identification. ECG analysis (depending on the type of the analysis) can contain several steps, such as preprocessing, feature extraction, feature selection, feature transformation and classification. Performing each step is crucial for the sake of the related analysis. In addition, the employed success measures and appropriate constitution of the ECG signal database play important roles in the analysis as well. In this work, the literature on ECG analysis, mostly from the last decade, is comprehensively reviewed based on all of the major aspects mentioned above. Each step in ECG analysis is briefly described, and the related studies are provided.
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We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogrambased heart rate and heart rate variability estimation Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and 9 [ms], respectively. The proposed algorithm 1) can be applied to any set of inertial sensors; 2) does not require access to any additional sensor modalities; 3) does not make any assumptions on the seismocardiogram morphology; and 4) explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well-suited for low-cost implementations using off-the-shelf inertial sensors and targeting e.g., at-home medical services.
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Among adults, depression is associated with reduced vagal activity, as indexed by high frequency heart rate variability [HF-HRV]), which correlates inversely with depression severity. Available evidence in depressed children and adolescents remains to be reviewed systematically. A search of the literature was performed to identify studies reporting (i) HF-HRV in clinically depressed children/adolescents relative to controls (k = 4, n = 259) and (ii) the association between HF-HRV and depressive symptoms as measured by standardized psychometric instruments in children and adolescents (k = 6, n = 2625). Random-effects meta-analysis on group differences revealed significant effects that were associated with a moderate effect size (Hedges' g = − 0.59; 95% CI [− 1.05; − 0.13]), indicating lower resting state HF-HRV among clinically depressed children/adolescents (n = 99) compared to healthy controls (n = 160), consistent with findings among adults. While no correlation between HF-HRV and depressive symptom severity was observed (r = −.041 [− 0.143; 0.062]), these additional correlational findings are limited to non-clinical samples. Findings have important clinical implications including a potentially increased risk for future physical ill health and also the identification of potential new treatment targets in child and adolescent depression.
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Methods: To evaluate the feasibility of the proposed indices, we apply these indices to study two different types of noxious stimulation, the endotracheal intubation and surgical skin incision, under general anesthesia. The performance was compared with traditional HRV indices, the heart rate reading and indices from electroencephalography. Results: The results indicate that the tvLF index performs best, and outperforms not only the traditional HRV index, but also the commonly used heart rate reading. Conclusion: With the help of ConceFT, the proposed HRV indices is potential to provide a better quantification of the dynamic change of the autonomic nerve system. Significance: Our proposed scheme of time-varying HRV analysis could contribute to the clinical assessment of analgesia under general anesthesia.
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Objective: The present work proposes a new epileptic seizure prediction method through integrating heart rate variability (HRV) analysis and an anomaly monitoring technique. Methods: Because excessive neuronal activities in the preictal period of epilepsy affect the autonomic nervous systems and autonomic nervous function affects HRV, it is assumed that a seizure can be predicted through monitoring HRV. In the proposed method, eight HRV features are monitored for predicting seizures by using multivariate statistical process control (MSPC), which is a well-known anomaly monitoring method. Results: We applied the proposed method to the clinical data collected from fourteen patients. In the collected data, eight patients had a total of eleven awakening preictal episodes and the total length of interictal episodes was about 57 hours. The application results of the proposed method demonstrated that seizures in ten out of eleven awakening preictal episodes could be predicted prior to the seizure onset, that is, its sensitivity was 91%, and its false positive rate was about 0.7 times per hour. Conclusion: This work proposed a new HRV-based epileptic seizure prediction method, and the possibility of realizing an HRV-based epileptic seizure prediction system was shown. Significance: The proposed method can be used in daily life, because the heart rate can be measured easily by using a wearable sensor.
Conference Paper
The study aimed to study the accuracy in RR time series derived from the seismocardiogram when employing different heartbeat detectors in subjects measured in a quiet environment. The ECG and seismocardiogram of 17 healthy volunteers was recorded at a sampling frequency of 5 kHz using a Biopac acquisition system. The seismocardiogram was acquired using a triaxial accelerometer (LIS344ALH, ST Microelectronics). Four detectors of the heartbeat from the seismocardiogram were employed relying either on the Continuous Wavelet Transform or bandpass filtering. The detectors adapt their parameters to the morphology of the signal by estimating mean heart rate and the bandwidth of the signal associated to the heartbeat. For all detectors, the standard deviation of the error in the obtained RR time series is in mean slightly higher than 2 ms and the percentage of obtained RR time intervals that have an error higher than 30 ms is around 3.5%. The seismocardiogram, when measured in a quiet environment, can be used instead of the ECG to obtain reliable RR time series when using proper heartbeat detectors.
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We propose and evaluate an unsupervised method for the estimation of heart rate variability (HRV) indices from ballistocardiograms (BCGs) recorded by a bed-mounted, electromechanical film (EMFi) sensor during sleep. After estimating the beat-to-beat intervals from the BCGs, short-term time- and frequency-domain HRV indices are computed and compared to an ECG reference. We evaluated signals recorded overnight from 8 subjects (approx. 212.000 heart beats). Our results show a good correlation (> 0.9) between BCG- and ECG- derived HRV indices and suggest that unsupervised long-term HRV monitoring using BCGs is indeed feasible.
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Background: The usefulness of heart rate variability (HRV) as a clinical research and diagnostic tool has been verified in numerous studies. The gold standard technique comprises analyzing time series of RR intervals from an electrocardiographic signal. However, some authors have used pulse cycle intervals instead of RR intervals, as they can be determined from a pulse wave (e.g. a photoplethysmographic) signal. This option is often called pulse rate variability (PRV), and utilizing it could expand the serviceability of pulse oximeters or simplify ambulatory monitoring of HRV. Methods: We review studies investigating the accuracy of PRV as an estimate of HRV, regardless of the underlying technology (photoplethysmography, continuous blood pressure monitoring or Finapresi, impedance plethysmography). Results/conclusions: Results speak in favor of sufficient accuracy when subjects are at rest, although many studies suggest that short-term variability is somewhat overestimated by PRV, which reflects coupling effects between respiration and the cardiovascular system. Physical activity and some mental stressors seem to impair the agreement of PRV and HRV, often to an inacceptable extent. Findings regarding the position of the sensor or the detection algorithm are not conclusive. Generally, quantitative conclusions are impeded by the fact that results of different studies are mostly incommensurable due to diverse experimental settings and/or methods of analysis.
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Heart rate variability (HRV) is an accepted and reliable means for assessing autonomic nervous system dysfunction. A 5-minute measurement of HRV is considered methodologically adequate. Several studies have attempted to use shorter recordings of 1-2 minutes or 10 seconds. The aim of this study was to determine the reliability of HRV parameters calculated from ultra-short electrocardiogram recordings. Seventy healthy volunteers were recruited for the study. HRV was evaluated for 5 minutes according to accepted procedures. Thereafter, HRV parameters were recalculated from randomly selected 1-minute and 10-second intervals. The standard and ultra-short measurements were correlated using intraclass correlation coefficients. Good correlations between the 5-minute electrocardiograms (ECGs) and both the 1-minute and 10-second ECGs were noted for average RR interval, and root mean square of successive differences in RR intervals (RMSSD). No correlation was noted for standard deviation of the RR interval (SDNN) and several other HRV parameters. RMSSD, but not SDNN, seem a reliable parameter for assessing HRV from ultra-short (1 minute or 10 seconds) resting electrocardiographic recordings. Power spectral analysis and evaluation of other HRV parameters require longer recording periods. Further research is required to evaluate the importance of ultra-short RMSSD for cardiovascular risk stratification.
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We investigate whether pulse rate variability (PRV) extracted from finger photo-plethysmography (Pleth) waveforms can be the substitute of heart rate variability (HRV) from RR intervals of ECG signals during obstructive sleep apnea (OSA). Simultaneous measurements (ECG and Pleth) were taken from 29 healthy subjects during normal (undisturbed sleep) breathing and 22 patients with OSA during OSA events. Highly significant (p<0.01) correlations (1.0>r>0.95) were found between heart rate (HR) and pulse rate (PR). Bland-Altman plot of HR and PR shows good agreement (<5% difference). Comparison of 2 min recording epochs demonstrated significant differences (p<0.01) in time, frequency domains and complexity analysis, between normal and OSA events using PRV as well as HRV measures. Results suggest that both HRV and PRV indices could be used to distinguish OSA events from normal breathing during sleep. However, several variability measures (SDNN, RMSSD, HF power, LF/HF and sample entropy) of PR and HR were found to be significantly (p<0.01) different during OSA events. Therefore, we conclude that PRV provides accurate inter-pulse variability to measure heart rate variability under normal breathing in sleep but does not precisely reflect HRV in sleep disordered breathing.