Seismocardiographic Signals Research Articles

Adaptive morphological modeling and optimal estimation for dynamic LVET measurement from seismocardiographic signals

The dynamic left ventricular ejection time (LVET) represents a crucial cardiovascular health indicator. Seismocardiography (SCG) offers substantial advantages over conventional techniques, however, it is susceptible to motion artifacts and exhibits complex morphologies, posing challenges for accurate dynamic LVET measurement. This study proposes a synergistic approach that integrates adaptive morphological modeling with an optimal estimation technique to accurately measure dynamic LVET from SCG signals. Firstly, an active feature recognition method was proposed to automatically identify 10 morphological features, followed by modeling them using 10 Gaussian functions. Subsequently, the model results were fused with measured SCG through optimal estimation, involving adaptive updating of the measurement noise matrix. When evaluated against state-of-the-art algorithms on the open-source CEBS dataset (82997 heartbeats) and the experimental dataset (34181 heartbeats), the proposed algorithm exhibits outstanding performance, with a goodness of fit (R2) of 0.9272 and root-mean-square error (RMSE) of 13.808 ms, convincingly demonstrating its effectiveness and advancement.

Publicly available signal databases containing seismocardiographic signals - the state in early 2023.

The development of information and communication technologies (ICT) changed many aspects of our lives, including cardiovascular research. This area of research is affected by the availability of open databases that can help conduct basic and applied research. In this study, we summarize the current state of knowledge in publicly available signal databases with seismocardiographic (SCG) signals in January 2023. Based on Google search results for the expression "seismocardiography dataset", we have found and described five databases with seismocardiograms, including three databases that contain SCG signals from healthy subjects, one database with data from porcine subjects, and one signal database with data obtained from human patients with valvular heart disease (VHD). All contain additional signals for reference points in the cardiac cycle. The most significant limitations of the described data sets are gender bias toward male subjects, the imbalance between healthy subjects, and subjects with two cardiovascular diseases (VHD and hemorrhage).

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.

Postural and longitudinal variability in seismocardiographic signals

Objective. Low frequency cardiovascular vibrations detectable on the chest surface (termed seismocardiography or SCG) may be useful for non-invasive diagnosis and monitoring of various cardiovascular conditions. A potential limitation of using SCG for longitudinal patient monitoring is the existence of intra-subject variability, which can contribute to errors in calculating SCG features. Improved understanding of the contribution of intra-subject variability sources may lead to improved SCG utility. This study aims to quantify postural and longitudinal SCG variability in healthy resting subjects during normal breathing. Approach. SCG and ECG signals were longitudinally acquired in 19 healthy subjects at different postures (supine, 45° head up, and sitting) during five recording sessions over five months. SCG cycles were segmented using the ECG R wave. Unsupervised machine learning was used to reduce SCG variability due to respiration by grouping the SCG signals into two clusters with minimized intra-cluster waveform heterogeneity. Several SCG features were assessed at different postures and longitudinally. Main results. SCG waveform morphological variability was calculated within each cluster (intra-cluster) and between two clusters (inter-cluster) at each posture and data collection session. The variabilities were significantly different between the supine and sitting but not between supine and 45° postures. For the 45° and sitting postures, the intra-cluster variability was not significantly different, while the inter-cluster variability difference was significant. The energy ratio between different frequency bands to total spectral energy in 0.5–50 Hz were calculated and were comparable for all postures. The combined cardiac timing intervals from the two clusters showed significant variation with postural changes. There was significant heart rate difference between the clusters and between postural positions. The SCG features were compared between longitudinal sessions and all features were not significantly different, Significance. Several SCG features significantly varied with posture suggesting that posture needs to be specified when comparing SCG changes over time. Longitudinally comparable SCG feature values suggests that significant longitudinal differences, if observed, may reflect true alternations in the cardiac functioning over time.

Accurate simultaneous measurement of heartbeat and respiratory intervals using a smartphone

In this paper, a method based on the usage of a smartphone for measuring simultaneously both heartbeat intervals and respiratory intervals is presented. In particular, the commodity accelerometer of a smartphone is used for measuring the seismocardiographic signal generated by heart activity and the acceleration due to breathing movements. The measurement is performed with the subject laying down, placing the smartphone on his/her xiphoid process. Signal processing algorithms are presented in the paper that can produce an accurate estimation of heartbeat and respiratory intervals from the measured acceleration signals. A metrological validation of the heartbeat and respiratory intervals estimates obtained with the proposed method is carried out by comparison with measurements obtained using an electrocardiograph and a spirometer. Two practical examples of applications of the measured quantities are finally reported, that are the measurement of the Heart Rate Variability from heartbeat intervals and of the Respiratory Sinus Arrhythmia from both the heartbeat intervals and the respiratory signal.

Respiratory Phase Detection From Seismocardiographic Signals Using Machine Learning

<h3>Introduction</h3> Seismocardiographic signals (SCG) have potential for HF monitoring and diagnosis. Respiratory variability of the SCG signal has been consistently observed. The objective of this study is to assess the accuracy of respiratory phase detection using SCG supervised machine learning in order to improve the accuracy of SCG for HF management. <h3>Methods</h3> SCG, electrocardiography (ECG) and spirometry were acquired in 15 healthy males (20-31 yo) during normal breathing. The SCG events were detected and segmented using the ECG R-wave. A machine learning technique termed "support vector machine" (SVM) was used to extract respiration phases from the SCG signal. The SVM algorithm finds a hyperplane in the "features space" such that the margin between the classes (i.e., respiration phases) is maximized. The SCG features utilized were the median waveform amplitude during contiguous 4 ms windows. The SCG events were labeled as inspiratory vs. expiratory depending on the SCG timing during the respiratory cycle. Subject-specific (SS) SVM training and testing was applied where data was split as 60 % training and 40 % testing for each subject. <h3>Results</h3> Figure 1 shows the testing accuracy for detecting inspiration vs expiration phases in each of the 15 subjects, demonstrating an average testing accuracy of 90.2 ± 6.5 %. <h3>Conclusion</h3> The results of this pilot study suggest that the utility of SCG for HF diagnosis and monitoring may be increased by a machine learning method to accurately account for respiratory phase changes during SCG signal analysis. Further investigation in HF subjects is needed to verify these findings.

Seismocardiographic Signal Variability During Regular Breathing And Breath Holding

<h3>Introduction</h3> Seismocardiography signals (SCG) are acoustic vibrations generated by heart activity and measured non-invasively at the chest surface. The potential utility of SCG for HF diagnosis and monitoring may be limited by signal variability. Respiration is a known, yet not well understood, source of variability. The objective of this pilot study was to quantify the SCG variability during breathing maneuvers. <h3>Methods</h3> SCG, electrocardiography (ECG) and airflow signals were acquired in two healthy subjects during normal breathing and breath holding (at end-inspiration and end-expiration while the glottis remained open). Data recording was repeated three times, while subjects resting on a 45 degree-elevated tilt table. The SCG events were detected and segmented utilizing the ECG R wave. Although the waveform varied, using unsupervised machine learning (k-medoid clustering) one could separate SCG waveforms during breathing into two "clusters" of similar events. The SCG variability was calculated using Dynamic Time Warping (DTW). The time interval between SCG1 and SCG 2 (which corresponds to the heart-sound ejection-period or HSEP) was also calculated. <h3>Results</h3> Table 1 shows the mean and standard deviation (SD) of the following SCG features: intra-cluster variability, inter-session variability and HSEP during regular breathing, breath hold at end inspiration and end expiration. SCG average intra-cluster and inter session variability was 27% and 24% lower, respectively, during breath holding for both end-inspiration and end-expiration compared to normal breathing. In addition, HSEP was 12% lower during breath holding compared to normal breathing. <h3>Conclusion</h3> Appropriate accounting for SCG variability introduced by breathing appears to account for a significant portion of the signal variability. Hence algorithms integrating respiratory changes may lead to more robust SCG analysis for HF diagnosis and monitoring. In addition, the observed respiration-dependent variability may prove to be a useful feature for HF management. Further studies are needed to confirm these findings in HF patients.

Analyzing seismocardiographic approach for heart rate variability measurement

As a vital risk stratification tool, heart rate variability (HRV) has the ability to provide early warning signs for many life-threatening diseases. This paper presents a study on reliable cardiac cycle extraction and HRV measurement with a seismocardiographic (SCG) method. Like R-peaks in an ECG, the proposed method relies on peaks corresponding to aortic valve opening (AO) instants in an SCG signal. Due to better reliability and accessibility, the SCG signal is selected for the study. Initially, the prominent AO peaks in an SCG signal are estimated using our previously proposed modified variational mode decomposition (MVMD) based approach. In the present method, the detection performance of AO peaks is improved by employing a decision-rule-based post-processing scheme. Subsequently, tachogram of AO–AO intervals is used for the estimation of HRV parameters. A set of real-time signals collected in various physiological conditions and the SCG signals taken from a publicly available standard database are used to test and validate the proposed method. Experimental results clearly tell that the cardiac intervals obtained from the SCG signal using the proposed method can be used for HRV analysis. Also, the resulted parameters of HRV analysis on ECG and SCG exhibit strong correlation and agreement that shows the effectiveness of the proposed method.

A seismocardiography system and a possibility of its use for diagnosis of internal organs diseases using seismocardiogram information analysis

The work is devoted to the development of a cardioseismometer system for obtaining a seismocardiogram for informational analysis of seismocardiographic signals and diagnosis of internal organs diseases on their basis. The cardioseismometer system allows measuring angular velocity projections and the vector of apparent acceleration recorded on the chest due to the mechanical movement of the heart, calculation of heartbeat acceleration vector - magnitude and direction relative to the reference coordinate system. The most informative and available for high-precision measurement parameters of seismocardiographic signals are selected, which are compared to the parameters of the electrocardiograms measuring model used to diagnose diseases. The conclusion was made about the possibility of using the dynamics of the seismocardiographic signal main parameters for informational analysis to diagnose diseases of internal organs. Experimental research results are given.

Heart Sound Extraction From Sternal Seismocardiographic Signal

The phonocardiogram (PCG) signal indicates closing instants of atrio-ventricular and semilunar valves, and this information can also be extracted from two major profiles of a seismocardiographic (SCG) cycle. This letter presents a method to extract fundamental heart sounds (HSs) from a SCG signal. The proposed method employs discrete wavelet transform for signal decomposition, and subsequently, center of gravity (CoG) of power spectrum for each of the subbands is computed. Our method can optimally select wavelet subbands for any combination of sampling frequencies of the signal and number of decomposition levels. Based on proposed CoG criterion, the signal is reconstructed from a number of selected subbands, and instantaneous Hilbert envelope is constructed for localizing peaks of the signal. Finally, a simple decision rule is applied to annotate S1 and S2 sounds of the PCG signal. Experimental results show that the HS waves S1 and S2 can be well localized with the help of one SCG cycle without using a reference ECG cycle.

Time-Frequency Distribution of Seismocardiographic Signals: A Comparative Study.

Accurate estimation of seismocardiographic (SCG) signal features can help successful signal characterization and classification in health and disease. This may lead to new methods for diagnosing and monitoring heart function. Time-frequency distributions (TFD) were often used to estimate the spectrotemporal signal features. In this study, the performance of different TFDs (e.g., short-time Fourier transform (STFT), polynomial chirplet transform (PCT), and continuous wavelet transform (CWT) with different mother functions) was assessed using simulated signals, and then utilized to analyze actual SCGs. The instantaneous frequency (IF) was determined from TFD and the error in estimating IF was calculated for simulated signals. Results suggested that the lowest IF error depended on the TFD and the test signal. STFT had lower error than CWT methods for most test signals. For a simulated SCG, Morlet CWT more accurately estimated IF than other CWTs, but Morlet did not provide noticeable advantages over STFT or PCT. PCT had the most consistently accurate IF estimations and appeared more suited for estimating IF of actual SCG signals. PCT analysis showed that actual SCGs from eight healthy subjects had multiple spectral peaks at 9.20 ± 0.48, 25.84 ± 0.77, 50.71 ± 1.83 Hz (mean ± SEM). These may prove useful features for SCG characterization and classification.

Motion noise cancellation in seismocardiogram of ambulant subjects with dual sensors.

This paper presents a dual-sensor method of extracting seismocardiographic (SCG) data from moving adult subjects using chest-worn wireless MEMS accelerometers. A digital signal processing (DSP) system including a normalized least means square (NLMS) adaptive filter is designed and tested in MATLAB. Data results from 10 subjects indicate a detection rate of 98.72% which outperforms our previously-proposed single-sensor scheme. Various sensor positons and possible failure mechanisms are also investigated to further evaluate the system performance. The results reveal that the quality of the SCG signal from moving subjects could be improved by integrating information from multiple sensors at the cost of increasing system complexity.

Cardiac-induced localized thoracic motion detected by a fiber optic sensing scheme.

The cardiovascular health of the human population is a major concern for medical clinicians, with cardiovascular diseases responsible for 48% of all deaths worldwide, according to the World Health Organization. The development of new diagnostic tools that are practicable and economical to scrutinize the cardiovascular health of humans is a major driver for clinicians. We offer a new technique to obtain seismocardiographic signals up to 54 Hz covering both ballistocardiography (below 20 Hz) and audible heart sounds (20 Hz upward), using a system based on curvature sensors formed from fiber optic long period gratings. This system can visualize the real-time three-dimensional (3-D) mechanical motion of the heart by using the data from the sensing array in conjunction with a bespoke 3-D shape reconstruction algorithm. Visualization is demonstrated by adhering three to four sensors on the outside of the thorax and in close proximity to the apex of the heart; the sensing scheme revealed a complex motion of the heart wall next to the apex region of the heart. The detection scheme is low-cost, portable, easily operated and has the potential for ambulatory applications.

Mechanisms Underlying Isovolumic Contraction and Ejection Peaks in Seismocardiogram Morphology.

A three-dimensional (3D) finite element electromechanical model of the heart is employed in simulations of seismocardiograms (SCGs). To simulate SCGs, a previously developed 3D model of ventricular contraction is extended by adding the mechanical interaction of the heart with the chest and internal organs. The proposed model reproduces the major peaks of seismocardiographic signals during the phases of the cardiac cycle. Results indicate that SCGs record the pressure of the heart acting on the ribs. In addition, the model reveals that the rotation of the rib with respect to the heart has a minor effect on seismocardiographic signal morphology during the respiratory cycle. SCGs are obtained from 24 human volunteers and their morphology is analyzed. Experimental results demonstrate that the peak of the maximum acceleration of blood in the aorta occurs at the same time as the global minimum of the SCG. It is confirmed that the first SCG peak after the electrocardiogram R-wave corresponds to aortic valve opening, as determined from the impedance cardiogram (p = 0.92). The simulation results reveal that the SCG peaks corresponding to aortic valve opening and the maximum acceleration of blood in the aorta result from ventricular contraction in the longitudinal direction of the ventricles and a decrease in the dimensions of the ventricles due to the ejection of blood, respectively.

Measurement of chest wall fibrations due to the activity of the heart.

ArticleMeasurement of chest wall fibrations due to the activity of the heart.A S Berson, and H V PipbergerA S Berson, and H V PipbergerPublished Online:01 Mar 1966https://doi.org/10.1152/jappl.1966.21.2.370MoreSectionsPDF (2 MB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInEmailWeChat Previous Back to Top Next Download PDF FiguresReferencesRelatedInformationCited ByTime-Frequency Distribution of Seismocardiographic Signals: A Comparative Study7 April 2017 | Bioengineering, Vol. 4, No. 4Physiological Motion and Measurement1 January 2016Vital signs modeling for Doppler radar cardiorespiratory monitoringThe cardiac impulse: A new look at an old artAmerican Heart Journal, Vol. 97, No. 1New phonocardiographic transducers utilizing the hot-wire anemometer principleMedical & Biological Engineering, Vol. 10, No. 1Biomedical Applications of a Commercial Capacitance TransducerIEEE Transactions on Biomedical Engineering, Vol. BME-16, No. 1 More from this issue > Volume 21Issue 2March 1966Pages 370-4 https://doi.org/10.1152/jappl.1966.21.2.370PubMed5934437History Published online 1 March 1966 Published in print 1 March 1966 Metrics