27 research outputs found

    Determining the Respiratory State From a Seismocardiographic Signal - A Machine Learning Approach

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    Seismocardiography (SCG) is a non-invasive method for measurement of vibrations on the chest wall originating from the heart. Respiration changes the morphology of the SCG-signal and analyzing these changes could improve the diagnostic value of SCG. This study aimed to determine the nasal respiration signal amplitude at mitral closure (MC) and aortic opening (AO) using SCG features. The three proposed methods for this were multiple regression analysis (MRA), support vector regression (SVR), and a neural network (NN). SCG, Electrocardiography and nasal-catheter flow signals were acquired from 18 healthy subjects (age 29± 6). SCG-signal fiducial points were used as features and were found using an automatic algorithm followed by manual verification. Fiducial points amplitudes, timings between these and frequency components formed 12 features. All models were trained on 80% of the data, underwent 10-fold cross-validation and were tested on the remaining 20% of the data. Predictions on test data for MC and AO time points, the Pearson correlations coefficient, and sum of squared errors of prediction were: (rMC, rAO, SSEMC, SSEAO) for the following models: NN (0.908, 0.904, 11.71, 12.05), SVR (0.881, 0.833, 18.95, 19.76) and MRA (0.450, 0.437, 51.21, 51.48). These predictive models show a strong correlation between the SCG-signal and respiration

    Characterization, Classification, and Genesis of Seismocardiographic Signals

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    Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG

    Seismocardiography:Interpretation and Clinical Application

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    Seismocardiography - Genesis, and Utilization of Machine Learning for Variability Reduction and Improved Cardiac Health Monitoring

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    Seismocardiography (SCG) is the measured chest surface vibrations resulting from cardiac activity. Although SCG can contain information that correlate with cardiac health, its utility may be limited by lack of understanding of the signal genesis and a variability that can mask subtle SCG changes. The current research utilized medical imaging reconstruction and finite element method (FEM) to simulate SCG by modeling the propagation of myocardial movements to the chest surface. FEM analysis provided a link between myocardial movements and the SCG signals measured at the chest surface and suggested that myocardial movement is a primary source of SCG. Increased understanding of the sources and propagation of SCG may help increase the utility of SCG as a cardiac monitoring tool. To reduce the variability of SCG measured in human subjects, unsupervised machine learning (ML) was implemented to group SCG beats into clusters with minimal intra-cluster heterogeneity. The clustering helped reduce the SCG variability and unveiled consistent relations with the respiratory phases and SCG morphology. This clustering reduced noise in calculating signal features and provided additional useful features. The study also analyzed longitudinal SCG from heart failure (HF) patients in order to predict HF readmission. Here, many time- and frequency-domain SCG features were extracted. Certain features showed good correlations with readmission. Using supervised ML algorithms, high classification accuracies (up to 100%) were achieved suggesting high SCG utility for monitoring HF patients and possibly other heart conditions. Effective monitoring followed by timely intervention can lead to improved patient management and reduced mortality

    Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 165, March 1977

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    This bibliography lists 198 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1977

    Modeling of flow generated sound in a constricted duct at low Mach number flow

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    Modelling flow and acoustics in a constricted duct at low Mach numbers is important for investigating many physiological phenomena such as phonation, generation of arterial murmurs, and pulmonary conditions involving airway obstruction. The objective of this study is to validate computational fluid dynamics (CFD) and computational aero-acoustics (CAA) simulations in a constricted tube at low Mach numbers. Different turbulence models were employed to simulate the flow field. Models included Reynolds Average Navier-Stokes (RANS), Detached eddy simulation (DES) and Large eddy simulation (LES). The models were validated by comparing study results with laser doppler anemometry (LDA) velocity measurements. The comparison showed that experimental data agreed best with the LES model results. Although RANS Reynolds stress transport (RST) model showed good agreement with mean velocity measurements, it was unable to capture velocity fluctuations. RANS shear stress transport (SST) k-{\omega} model and DES models were unable to predict the location of high fluctuating flow region accurately. CAA simulation was performed in parallel with LES using Acoustic Perturbation Equation (APE) based hybrid CAA method. CAA simulation results agreed well with measured wall sound pressure spectra. The APE acoustic sources were found in jet core breakdown region downstream of the constriction, which was also characterized by high flow fluctuations. Proper Orthogonal Decomposition (POD) is used to study the coherent flow structures at the different frequencies corresponding to the peaks of the measured sound pressure spectra. The study results will help enhance our understanding of sound generation mechanisms in constricted tubes including biomedical applications

    Modeling of flow generated sound in a constricted duct at low Mach number flow

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    Modelling flow and acoustics in a constricted duct at low Mach numbers is important for investigating many physiological phenomena such as phonation, generation of arterial murmurs, and pulmonary conditions involving airway obstruction. The objective of this study is to validate computational fluid dynamics (CFD) and computational aero-acoustics (CAA) simulations in a constricted tube at low Mach numbers. Different turbulence models were employed to simulate the flow field. Models included Reynolds Average Navier-Stokes (RANS), Detached eddy simulation (DES) and Large eddy simulation (LES). The models were validated by comparing study results with laser doppler anemometry (LDA) velocity measurements. The comparison showed that experimental data agreed best with the LES model results. Although RANS Reynolds stress transport (RST) model showed good agreement with mean velocity measurements, it was unable to capture velocity fluctuations. RANS shear stress transport (SST) k-ω model and DES models were unable to predict the location of high fluctuating flow region accurately. CAA simulation was performed in parallel with LES using Acoustic Perturbation Equation (APE) based hybrid CAA method. CAA simulation results agreed well with measured wall sound pressure spectra. The APE acoustic sources were found in jet core breakdown region downstream of the constriction, which was also characterized by high flow fluctuations. Proper Orthogonal Decomposition (POD) is used to study the coherent flow structures at the different frequencies corresponding to the peaks of the measured sound pressure spectra. The study results will help enhance our understanding of sound generation mechanisms in constricted tubes including biomedical applications

    Aerospace medicine and biology: A continuing bibliography with indexes, supplement 239, December 1982

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    This bibliography lists 318 reports, articles and other documents introduced into the NASA scientific and technical information system in November 1982

    Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

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    In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers
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