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Pulmonary diseases and injury lead to structural and functional changes in the lung parenchyma and airways, often resulting in measurable sound transmission changes on the chest wall surface. Additionally, noninvasive imaging of externally driven mechanical wave motion in the chest (e.g., using magnetic resonance elastography) can provide information about lung stiffness and other structural property changes which may be of diagnostic value. In the present study, a comprehensive computational simulation (in silico) model was developed to simulate sound wave propagation in the airways, parenchyma, and chest wall under normal and pathological conditions that create distributed structural (e.g., pneumothoraces) and diffuse material (e.g., fibrosis) changes, as well as a localized structural and material changes as may be seen with a neoplasm. Experiments were carried out in normal subjects to validate the baseline model. Sound waves with frequency content from 50 to 600 Hz were introduced into the airways of three healthy human subjects through the mouth, and transthoracic transmitted waves were measured by scanning laser Doppler vibrometry at the chest wall surface. The computational model predictions of a frequency-dependent decreased sound transmission due to pneumothorax were consistent with experimental measurements reported in previous work. Predictions for the case of fibrosis show that while shear wave motion is altered, changes to compression wave propagation are negligible, and thus insonification, which primarily drives compression waves, is not ideal to detect the presence of fibrosis. Results from the numerical simulation of a tumor show an increase in the wavelength of propagating waves in the immediate vicinity of the tumor region., Graphical Abstract

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.

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.

Introduction Minimally invasive intracranial pressure (ICP) screening has long been desired by neurosurgeons. A novel approach deriving ICP from tympanic membrane (TM) pulsation may offer the solution. The ICP waveform appears to be transmitted to the TM by the cochlear aqueduct. The resulting TM infrasonic pulsations can be measured by certain sensors. Elevated ICP alters brain compliance, which appears to yield slower rise times of the TM pulsation waveform. Measurement of this change may be useful in screening for elevated ICP. This paper investigates one such technique. Methods A stethoscope was modified for airtight external ear canal fit; the dome was exchanged for a magnetic reluctance pressure sensor, allowing measurement of TM pulsations. Analog TM pulsations were analyzed by measuring the pulsation's slope ratio between the waveform's downslope and upslope. Seventeen normal subjects (ages 18-32 years) underwent hyperventilation and tilt table testing to induce ICP changes. An algorithm processed this data and predicted the subject's ICP status. Results The slope ratio method showed consistent and stable changes with the expected alterations in ICP from the tilt test and hyperventilation maneuvers. The classification algorithm correctly identified subjects with elevated ICP in 60 of 60 independent recordings on 17 subjects. Conclusion This paper has four conclusions. First, the "brain stethoscope" can detect increased ICP from the TM pulsation waveform in healthy subjects. Second, analysis of the TM waveform using slope ratio calculations is capable of distinguishing normal versus elevated ICP. Third, the tilt and hyperventilation maneuvers showed the expected physiologic trends. Last, further studies are needed on patients with pathological ICP before the brain stethoscope can be implemented into clinical practice.

Elevated intracranial pressure (ICP) is a major neurological issue that can occur in traumatic brain injury, cerebral edema, subarachnoid hemorrhage, intracerebral hemorrhage, hydrocephalus, etc. The normal range of ICP is 5–15 mmHg for healthy adults [1]. Elevated ICP (>20 mmHg) can cause serious complications including seizures and death. The current gold standards for ICP measurement require a high level of expertise and are invasive, costly and associated with intracranial infection risks [2]. Therefore, noninvasive monitoring of ICP would be useful for improved patient diagnosis and management. Previous studies [3]-[4] have suggested potential utility of tympanic membrane pulsation (TMp) measurements for detection of elevated ICP. The current study reports consistent TMp waveform and spectral changes with elevated ICP.

Timely detection of Developmental Dysplasia of the hip (DDH) in infants and children is crucial as DDH can lead to permanent hip instability if diagnosis is delayed [1]. Existing methods of DDH detection, such as ultrasound and x-rays, are expensive and require trained medical personnel to perform and interpret the tests. Furthermore, x-rays subject patients to ionizing radiation with its attendant cancer risks, especially problematic when repeat studies are performed. In the current study, an acoustic non-invasive approach for DDH detection in a postmortem pig model is presented. We previously designed and tested a similar approach in different simplified benchtop models of the hip joint [2]. A compact system to assess sound transmission through joints was also tested [3] [4]. Moreover, physics-based computational studies were carried out to explain biomechanical factors determining the reduction of dislocated hips [5] [6] [7].

Seismocardiographic signals (SCG) are known to correlate with mechanical cardiac activity and may be used for monitoring patients with cardiovascular disease. However, SCG variability is not well understood and may interfere with signal utility. In the current study, the SCG signals were acquired in 5 healthy subjects during regular breathing along with ECG and respiratory flow measurements. In addition, SCG waveforms were recorded during breath hold at end inspiration as well as end expiration. The SCG events were identified and segmented using ECG events. SCG waveforms during regular breathing were separated into two clusters using unsupervised machine learning. The variability was assessed for the clustered and un-clustered SCG by analyzing the Dynamic Time Warping (DTW) distances of SCG waveforms in the time domain. The inter-group variability between the normal breathing clusters and breath hold suggested that cluster 2 events were more similar to end expiration events while no clear trend was observed for cluster 1. The intra-group variability was reduced by approximately 19% for regular breathing clusters and 42% during breath hold compared to the unclustered SCG during normal breathing. The reduced variability during breath hold suggests the utility of SCG recording at breath hold since variability reduction can lead to more robust methods for longitudinal patient monitoring.

Elevated intracranial pressure (ICP) can lead to serious health complications. Hence, this pressure needs to be monitored in patients at risk of increased ICP. The gold standard for ICP measurements are invasive manometers and pressure transducers [1] . However, the risks, discomforts, and expenses of invasive diagnostic can be avoided if satisfactory non-invasive approaches are used. In this presentation, a noninvasive method of monitoring ICP utilizing measurements of Tympanic Membrane pulsation (TMp) is discussed. TMp signals were acquired from 5 healthy subjects at different tilt positions where ICP is expected to increase with head-down positioning. Consistent TMp waveform morphological changes were observed in each subject with the head down position, which is known to increase ICP [2] . The changes tended to reverse with hyperventilation, which is a process known to decrease ICP [3] . These results suggest that TMp waveform measurements may provide a reliable non-invasive method for monitoring ICP.

Sound transmission in the human body can be affected by the tissue composition along the sound path and surrounding structures. Therefore, acoustic transmission may correlate with pathologies involving structural changes. Previous studies utilized sound transmission to detect a variety of pulmonary, gastrointestinal, vascular, cardiac conditions, and developmental dysplasia of the hip (DDH) [1] [2] [3] [4] [5] [6] . The objective of this study is to design and test a reliable system capable of providing adequate acoustic stimulus, and simultaneously measure transmitted signals at multiple skin surface locations. The study objectives include determining: (1) the static load needed to reach a target SNR (>20 dB) at the measurement points and a target coherence (>0.8) between excitation and measurement points; (2) the exciter sensitivity to static load changes; and (3) the exciter input maximum power and corresponding acceleration. These results will help guide the choice of optimal exciter that: (1) can withstand sufficient static load (~500g), which would provide coupling to the bone to reach a target SNR and coherence; (2) has low sensitivity to load (low variability for a load change ~100 gm); (3) can provide sufficient acoustic excitation energy to maintain the target SNR and coherence; (4) be available at a reasonable cost (~

Respiration is commonly monitored using direct measurement of airflow with a mouthpiece or other access to breathing airflow. Indirect methods, such as those based on electrocardiography or chest impedance, may be advantageous especially if they already need to be performed. When direct access to breathing airflow is difficult or inconvenient an indirect approach may be needed. Some physiological signals are known to be affected by respiration, suggesting possible utility for extracting a respiratory signal from these signals. In the current study, the performance of several non-invasive methods for respiratory flow monitoring was compared. The study investigated the correlation between lung volume changes and other physiological signals including: chest wall movement (WM), chest galvanic skin response (GSR) and seismocardiographic (SCG) signals. Respiratory airflow was simultaneously measured along with the proposed candidate modalities. The measured airflow signal was integrated to determine lung volume changes, which was chosen as the reference signal. A respiratory signal was extracted from each of the proposed modalities and compared with the lung volume signal. Using airflow measurements, the average respiratory rate was found to be 13.8 breath per minute in the study subjects. Comparisons of the respiratory rate from different methods suggested that the respiratory rate was successfully detected by all proposed methods (accuracy between 85-100%). The ability of different methods to detect the correct respiratory phase in the time domain varied. Among the proposed methods, GSR was found to have the highest agreement with the reference method in respiratory phase detection (94-98%). This indicates that GSR may be used to accurately identify both respiratory rate and lung volume phases. One important GSR benefit is that it only requires chest surface sensors, which would offer operational advantages when direct access to breathing airflow stream is undesirable.

Seismocardiographic (SCG) signal morphology is known to be affected by cardio-pulmonary interactions, which introduce variability in the SCG signal. Hence, grouping of SCG signals according to their respiratory phase can reduce their morphological dissimilarity. In addition, correlating SCG with pulmonary phases may provide more insights into the nature of cardio-pulmonary interactions. This study uses unsupervised machine learning to cluster SCG events based on their morphology. Here, K-means clustering was employed using the time domain amplitude as the feature vector. The method is applied on measured SCG data from 5 male subjects (Age: 30 ± 5.8 years). The mean Silhouette values for different number of clusters suggested that optimal clustering was reached when SCG waveforms were divided into two groups. Using respiratory flow information, SCG waves were labeled as inspiratory vs. expiratory or high vs. low lung volume. The SCG clusters were then compared with these labels and purity values were calculated. The distributions of clustered SCG events in relation to respiratory flowrate and lung volume phases showed consistent trends in all subjects. Results suggested that grouping SCG based on lung volume phases would yield more homogeneous groups and, hence, would keep SCG variability (within each group) to a minimum. The demonstrated utility of the proposed machine learning approach in identifying respiratory phases from SCG waveforms may obviate the need for simultaneous respiratory measurements.

This study aims at developing a digital signal processing algorithm to extract positive and negative peak velocity profiles from Doppler echocardiographic images. These profiles are useful in estimating cardiac time intervals and establishing realistic boundary conditions for computational hemodynamic studies. The proposed image processing algorithm is based on two different thresholding methods. The histograms of image intensity function were used to help threshold values selection so that the algorithm yields velocity profiles properly represent Doppler shift envelopes. One of the thresholding methods tended to provide the lower-limit (i.e. underestimate) of the velocity profile, while the second tended to provide the upper-limit of the velocity profile (i.e., overestimate). The final peak velocity profiles were estimated from the combination of the estimates from both thresholding methods. The peak velocity profiles were then qualitatively compared with the results of the standard edge detection methods such as Canny and Prewitt approximations. The proposed automated approach might be helpful for objective estimation of peak velocities and cardiac time intervals.

An acoustic non-invasive approach for detection of Developmental Dysplasia of Hip (DDH) was investigated. The proposed method was tested using different benchtop simplified models of the hip joint. Models were stimulated with band-limited white acoustic noise (10-2500 Hz) and the response of the model was measured. The transfer function, coherence, and phase were determined for different simulated hip dysplasia levels and for simulated normal cases. Results showed that the transfer function, coherence and phase were affected by dysplasia occurrence. Larger effects were seen for more simulated severe dysplastic hips. This suggests that the proposed approach may have potential for DDH detection. Further investigations in animal models and humans are warranted to document accuracy of the proposed approach.

Introduction Seismocardiography (SCG) includes vibration signals propagated to the chest surface from cardiac valve movement, blood flow and myocardial muscle contraction. SCG feature analysis may prove useful in the early non-invasive detection of HF deterioration. In this study, the propagation and coupling of cardiac surface motion to the anterior chest surface was modeled using computer simulations. Objective Develop a computational model of SCG propagation to enhance understanding of SCG genesis, and SCG's spatial amplitude and spectrographic distribution over the anterior chest wall. Methods The computational geometry was extracted from short-axis cardiac cine MRI imaging of a healthy participant. Cardiac wall motion was captured by applying a 3D motion tracking algorithm to the cine MRI images. The propagation of cardiac motion to the chest surface was simulated using Finite Element Method (FEM). A mechanical model was also developed where the displacements at a simulated heart wall were coupled to a mechanical chest surface model using an array of linear actuators. Results Common SCG feature points (e.g., MC-mitral valve closure; IM- Isovolumic contraction; AO- aortic valve opening; AC- aortic valve closure; and MO- mitral valve opening) were predicted by the model. Furthermore, the timing of these feature points was validated against the left ventricle volume variation extracted from short-axis cine MRI images. The maximum SCG amplitude (in the dorso-ventral direction) was observed in the 4th intercoastal space near the left lower sternal border. The pilot results of the mechanical model were in a qualitative agreement. Conclusions The computer model predicted the feature points observed in real SCG measurements. The current model may be utilized in the future to study the distribution of SCG over precordium and to analyze the effect of the properties of cardiac and contiguous chest structures, including great vessels, chest wall ribs, muscles and articulations, lungs, and cutaneous soft tissue. Further model development may deepen understanding of SCG generation and propagation, increasing its potential utility for diagnosis and monitoring of cardiac pathologies.

Introduction Seismocardiography (SCG) (low frequency cardiovascular vibrations acquired on the chest wall) has potential for early noninvasive detection of heart failure (HF) deterioration. However, inaccurate sensor placement may significantly limit the method's utility. Objective To document SCG waveform spatial variability to aid in sensor placement and quantification of sensor position shifts on the signal reproducibility. Methods SCG signals were recorded in 15 healthy subjects using 36 accelerometers placed on the anterior chest wall along with simultaneous ECG and respiratory flow measurements (Figure 1). SCG waveforms were segmented using ECG beats. SCG feature points were identified according to previous studies and chest surface acceleration maps created. Results Figure 2 suggests that SCG may be loudest and positive (i.e., anterior or outward) acceleration at the left lower sternal border (LLSB) during the aortic opening and closure periods. A negative (i.e. posterior or inward) acceleration was observed around the LLSB location during the isovolumic contraction time period. The SCG intra-group variability with change in sensor position is shown in Table 1.Table 1: SCG intra-group variability with sensor position change Conclusions Optimal sensor location was found around the LLSB between 3rd to 5th ICS. Use of this location may help increase robustness of SCG signal interpretation and its utility for HF monitoring. Further, change in sensor locations of 3 cm around 4thICS at LLSB (the target sensor location) resulted in only a ∼7 % change in waveform variability and ∼9 % change in PEP, which if true, would better facilitate ambulatory use and sensor placement by patients.

In this study, the sound generation in a constricted pipe at low Mach number (M=0.003, Re=1170 at the inlet) flow is investigated using a hybrid modeling approach of computational aero-acoustics (CAA). Large Eddy Simulation (LES) model is used simulate the flow and accurately capture the flow instabilities associated with the constriction. The Acoustic Perturbation Equation (APE) method is used to model the acoustic field where the source terms are derived from the incompressible flow simulation results. The acoustic spectra from the APE simulation results showed good agreement (within 9%) with measured values on the pipe wall downstream of the constriction. The strongest APE acoustic sources were found to occur in the jet core breakdown region that contained the highest pressure fluctuations in the flow field. Proper Orthogonal Decomposition (POD) was used to study the Coherent flow structures in the jet core breakdown region in order to further understand the characteristics of acoustic sources. The strongest structure corresponds to the peak frequency of the measured sound pressure spectra. The study results improve our understanding of the sound generation mechanisms in biomedical applications involving the airways such as phonation and partial airway obstruction.

Artificial heart valves may dysfunction, leading to thrombus and/or pannus formations. Computational fluid dynamics is a promising tool for improved understanding of heart valve hemodynamics that quantify detailed flow velocities and turbulent stresses to complement Doppler measurements. This combined information can assist in choosing optimal prosthesis for individual patients, aiding in the development of improved valve designs, and illuminating subtle changes to help guide more timely early intervention of valve dysfunction. In this computational study, flow characteristics around a bileaflet mechanical heart valve were investigated. The study focused on the hemodynamic effects of leaflet immobility, specifically, where one leaflet does not fully open. Results showed that leaflet immobility increased the principal turbulent stresses (up to 400%), and increased forces and moments on both leaflets (up to 600% and 4000%, respectively). These unfavorable conditions elevate the risk of blood cell damage and platelet activation, which are known to cascade to more severe leaflet dysfunction. Leaflet immobility appeared to cause maximal velocity within the lateral orifices. This points to the possible importance of measuring maximal velocity at the lateral orifices by Doppler ultrasound (in addition to the central orifice, which is current practice) to determine accurate pressure gradients as markers of valve dysfunction.

Inspiratory and expiratory flow in a multi-generation pig lung airway was numerically studied at a peak tracheal flow rate corresponding to a Reynolds number of 1150. The model was validated by comparing velocity distributions with previous measurements for a simple airway bifurcation. Simulation results at different cross sections of the airway tree provided detailed maps of the axial and secondary flow patterns. Flow at the main bifurcation and in many other bifurcations showed complex secondary flow structures. The flow morphology in the pig airways differed from that of simplified bifurcation airway models and that of humans, which is likely due to the large differences in the airway geometry of the different species. The inspiratory pressure drop was calculated, and simulation results suggested that the viscous pressure drop values were comparable to earlier studies in human airway geometries. The reported differences between pig and human airways need to be taken into consideration when generalizing results of animal experiments to humans.

Monitoring intracranial pressure (ICP) is vital for patients with elevated, or potentially elevated ICP. This pressure can be monitored using invasive procedures such as lumbar puncture manometry or various methods of direct measurement in or upon the brain, each with attendant complication risks. Measurement of naturally occurring tympanic membrane pulse (TMp) may provide an alternative non-invasive method of monitoring ICP, which would help the risks of invasive methodologies. This paper discusses a piezo based sensor (which we term the “brain stethoscope”) designed and tested to acquire TMp signals. In addition, the TMp signals were acquired from five human subjects where ICP was expected to vary. ICP was increased in this experimental model using head down positioning on a tilt table. Results showed that tympanic membrane waveform changed in morphology and amplitude with increased ICP. The lead time between the TMp signal and a reference signal (Ear lobe blood flow pulse) was found to increase as ICP increased using this model. We conclude that measurement of TMp changes may provide a new non-invasive, low cost and easy to perform technique for monitoring patients at risk of elevated ICP.

A new computational approach for simulating the blood flow induced sound generation and propagation in a stenosed artery with one-sided constriction was investigated. This computational hemoacoustic method is based on mapping the transient pressure (force) fluctuations on the vessel wall and solving for the structural vibrations in frequency domain. These vibrations were detected as sound on the epidermal surface. The current hydro vibroacoustic method employs a two step, one way coupled approach for the sound generation in the flow domain and its propagation through the tissue layers. The results were validated by comparing with previous analytical and computational solutions. It was found that the bruits (generated from the flow around the stenosis) are related primarily to the time derivative of the integrated pressure force on the arterial wall downstream of the stenosis. Advantages of the methods used in the current study include: (a) capability of providing accurate solution with a faster solution time; (b) accurately capturing the break frequency of the velocity fluctuation measured on epidermal surface; (c) inclusion of the fluid structure interaction between blood flow and the arterial wall.

Hydrocephalus is a medical condition characterized by an excessive accumulation of cerebrospinal fluid (CSF) in the ventricles of the brain, typically causing an increase in patient intracranial pressure (ICP). The increased ICP can result in headache, nausea, vomiting, and even death. To combat this potentially dangerous elevated ICP, physicians often use surgically implemented brain shunt systems that drain excess CSF from the brain into the abdominal cavity. Monitoring of ICP in patients with hydrocephalus is crucial for the effective management of brain shunt systems. Currently, clinical ICP measurement approaches are typically invasive and are performed using intraventricular catheters placed through a drilled hole in the skull. Home monitoring of ICP may be advantageous for tracking patient clinical status. A noninvasive method would be particularly useful for ICP monitoring in the emergency department, doctor's office, home and other non-ICU settings. This study aims to explore the correlation between ICP and tympanometric parameters including static acoustic compliance, a measurement of middle ear pressure at maximum compliance. Tympanometry was performed on subjects who rested on a tilt table at varying tilt angles, which induces changes in ICP. Results suggested a correlation between tilt angles and pressure of the middle ear at peak compliance. Tympanometry may provide a noninvasive method for monitoring ICP.

Seismocardiography (SCG) is a non-invasive method that can be used for cardiac activity monitoring. This paper presents a new electrocardiogram (ECG) independent approach for estimating heart rate (HR) during low and high lung volume (LLV and HLV, respectively) phases using SCG signals. In this study, SCG, ECG, and respiratory flow rate (RFR) signals were measured simultaneously in 7 healthy subjects. The lung volume information was calculated from the RFR and was used to group the SCG events into low and high lung-volume groups. LLV and HLV SCG events were then used to estimate the subjects HR as well as the HR during LLV and HLV in 3 different postural positions, namely supine, 45 degree heads-up, and sitting. The performance of the proposed algorithm was tested against the standard ECG measurements. Results showed that the HR estimations from the SCG and ECG signals were in a good agreement (bias of 0.08 bpm). All subjects were found to have a higher HR during HLV $(\mathbf{HR}_{\mathbf{HLV}})$ compared to LLV $(\mathbf{HR}_{\mathbf{LLV}})$ at all postural positions. The $\mathbf{HR}_{\mathbf{HLV}}/\mathbf{HR}_{\mathbf{LLV}}$ ratio was $\mathbf{1.11}\pm \mathbf{0.07} \mathbf{1.08}\pm \mathbf{0.05},\ \mathbf{1.09}\pm \mathbf{0.04}$ , and $1.09\pm 0.04\ (\mathbf{mean}\pm \mathbf{SD})$ ) for supine, 45 degree-first trial, 45 degree-second trial, and sitting positions, respectively. This heart rate variability may be due, at least in part, to the well-known respiratory sinus arrhythmia. HR monitoring from SCG signals might be used in different clinical applications including wearable cardiac monitoring systems.

Inspiratory flow in a multigeneration pig lung airways was numerically studied at a steady inlet flow rate of 3.2 × 10−4 m3/s corresponding to a Reynolds number of 1150 in the trachea. The model was validated by comparing velocity distributions with previous measurements and simulations in simplified airway geometries. Simulation results provided detailed maps of the axial and secondary flow patterns at different cross sections of the airway tree. The vortex core regions in the airways were visualized using absolute helicity values and suggested the presence of secondary flow vortices where two counter-rotating vortices were observed at the main bifurcation and in many other bifurcations. Both laminar and turbulent flows were considered. Results showed that axial and secondary flows were comparable in the laminar and turbulent cases. Turbulent kinetic energy (TKE) vanished in the more distal airways, which indicates that the flow in these airways approaches laminar flow conditions. The simulation results suggested viscous pressure drop values comparable to earlier studies. The monopodial asymmetric nature of airway branching in pigs resulted in airflow patterns that are different from the less asymmetric human airways. The major daughters of the pig airways tended to have high airflow ratios, which may lead to different particle distribution and sound generation patterns. These differences need to be taken into consideration when interpreting the results of animal studies involving pigs before generalizing these results to humans.

Computational fluid dynamics (CFD) is a useful tool for prediction of turbulence in aerodynamic and biomedical applications. The choice of appropriate turbulence models is key to reaching accurate predictions. The present investigation concentrated on the comparison of different turbulence models for predicting the flow field downstream of a constricted pipe. This geometry is relevant to arterial stenosis in patients with vascular diseases. More specifically, the results of Unsteady Reynolds-Averaged Navier-Stokes (URANS) and scale resolving simulation (SRS) turbulence models such as Large Eddy Simulation (LES) and Detached Eddy Simulation (DES) were compared with experimental measurements. Comparisons included the mean flow and fluctuations downstream of the constriction. Results showed that the LES model was in better agreement with the velocity measurements performed using a Laser Doppler Anemometry (LDA). In addition, although URANS models predicted a wake region size and mean flow velocities comparable to SRS turbulence models, no small-scale vortical structures can be observed in the URANS solution due to the nature of these models. Modeling of these structures would, however, be helpful when more detailed flow behavior is needed such as in studies of acoustic sources. Hence, LES would be an optimal turbulence model for the flow under consideration, especially when sound generation would be of interest.

Speckle Tracking Echocardiography (STE) is a relatively new method for cardiac function evaluation. In the current study, STE was used to investigate the timing of heart-induced mostly subaudible (i.e., below the frequency limit of human hearing) chest-wall vibrations in relation to the longitudinal myocardial strain. Such an approach may help elucidate the genesis of these vibrations, thereby improving their diagnostic value.

As the heart beats, it creates fluctuation in blood pressure leading to a pulse wave that propagates by displacing the arterial wall. These waves travel through the arterial tree and carry information about the medium that they propagate through as well as information of the geometry of the arterial tree. Pulse wave velocity (PWV) can be used as a non-invasive diagnostic tool to study the functioning of cardiovascular system. A stenosis in an artery can dampen the pulse wave leading to changes in the propagating pulse. Hence, PWV analysis can be performed to detect a stenosed region in arteries. This paper presents a numerical study of pulse wave propagation in a stenosed artery by means of two-way coupled fluid structure interaction (FSI). The computational model was validated by the comparison of the simulated PWV results with theoretical values for a healthy artery. Propagation of the pulse waves in the stenosed artery was compared with healthy case using spatiotemporal maps of wall displacements. The analysis for PWV showed significance differences between the healthy and stenosed arteries including damping of propagating waves and generation of high wall displacements downstream the stenosis caused by flow instabilities. This approach can be used to develop patient-specific models that are capable of predicting PWV signatures associated with stenosis changes. The knowledge gained from these models may increase utility of this approach for managing patients at risk of stenosis occurrence.

This paper proposes a novel adaptive feature extraction algorithm for seismocardiographic (SCG) signals. The proposed algorithm divides the SCG signal into a number of bins, where the length of each bin is determined based on the signal change within that bin. For example, when the signal variation is steeper, the bins are shorter and vice versa. The proposed algorithm was used to extract features of the SCG signals recorded from 7 healthy individuals (Age: 29.4$\pm$4.5 years) during different lung volume phases. The output of the feature extraction algorithm was fed into a support vector machines classifier to classify SCG events into two classes of high and low lung volume (HLV and LLV). The classification results were compared with currently available non-adaptive feature extraction methods for different number of bins. Results showed that the proposed algorithm led to a classification accuracy of ~90%. The proposed algorithm outperformed the non-adaptive algorithm, especially as the number of bins was reduced. For example, for 16 bins, F1 score for the adaptive and non-adaptive methods were 0.91$\pm$0.05 and 0.63$\pm$0.08, respectively.

There are more than 300,000 heart valves implanted annually worldwide with about 50% of them being mechanical valves. The heart valve replacement is often a common treatment for severe valvular disease. However, valves may dysfunction leading to adverse hemodynamic conditions. The current computational study investigated the flow around a bileaflet mechanical heart valve at different leaflet dysfunction levels of 0%, 50%, and 100%, and documented the relevant flow characteristics such as vortical structures and turbulent shear stresses. Studying the flow characteristics through these valves during their normal operation and dysfunction can lead to better understanding of their performance, possibly improved designs, and help identify conditions that may increase the potential risk of blood cell damage. Results suggested that maximum flow velocities increased with dysfunction from 2.05 to 4.49 ms-1 which were accompanied by growing eddies and velocity fluctuations. These fluctuations led to higher turbulent shear stresses from 90 to 800 N.m-2 as dysfunctionality increased. These stress values exceeded the thresholds corresponding to elevated risk of hemolysis and platelet activation. The regions of elevated stresses were concentrated around and downstream of the functional leaflet where high jet velocity and stronger helical structures existed.

Bileaflet mechanical heart valves have one of the most successful valve designs for more than 30 years. These valves are often used for aortic valve replacement, where the geometry of the aortic root sinuses may vary due to valvular disease and affect valve performance. Common geometrical sinus changes may be due to valve stenosis and insufficiency. In the current study, the effect of these geometrical changes on the mean flow and velocity fluctuations downstream of the valve and aortic sinuses were investigated. The study focused on the fully-open leaflet position where blood velocities are close to their maximum. Simulation results were validated using previous experimental laser Doppler anemometry (LDA) measurements. Results showed that as the stenosis and insufficiency increased there were more flow separation and increased local mean velocity downstream of the leaflets. In addition, the detected elevated velocity fluctuations were associated with higher Reynolds shear stresses levels, which may increase the chances of blood damage and platelet activation and may lead to increased risk of blood clot formation.

In this study, a machine learning algorithm was developed to classify seismocardiographic (SCG) signals occurring during low and high lung volumes. The results demonstrated that morphological differences can be observed in SCG waveforms during respiration. SCG events were classified using a Radial Basis Function (RBF) support vector machine (SVM) algorithm into the two classes of low and high lung volume. Classification accuracy was found to be about 75%.

Seismocardiographic (SCG) signals are chest surface vibrations induced by cardiac activity. 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. One approach of determining signal features is to estimate its time-frequency characteristics. In this regard, four different time-frequency distribution (TFD) approaches were used including short-time Fourier transform (STFT), polynomial chirplet transform (PCT), Wigner-Ville distribution (WVD), and smoothed pseudo Wigner-Ville distribution (SPWVD). Synthetic SCG signals with known time-frequency properties were generated and used to evaluate the accuracy of the different TFDs in extracting SCG spectral characteristics. Using different TFDs, the instantaneous frequency (IF) of each synthetic signal was determined and the error (NRMSE) in estimating IF was calculated. STFT had lower NRMSE than WVD for synthetic signals considered. PCT and SPWVD were, however, more accurate IF estimators especially for the signal with time-varying frequencies. PCT and SPWVD also provided better discrimination between signal frequency components. Therefore, the results of this study suggest that PCT and SPWVD would be more reliable methods for estimating IF of SCG signals. Analysis of actual SCG signals showed that these signals had multiple spectral components with slightly time-varying frequencies. More studies are needed to investigate SCG spectral properties for healthy subjects as well as patients with different cardiac conditions.

Seismocardiography (SCG) offers a potential non-invasive method for cardiac monitoring. Quantification of the effects of different physiological conditions on SCG can lead to enhanced understanding of SCG genesis, and may explain how some cardiac pathologies may affect SCG morphology. In this study, the effect of the respiration on the SCG signal morphology is investigated. SCG, ECG, and respiratory flow rate signals were measured simultaneously in 7 healthy subjects. Results showed that SCG events tended to have two slightly different morphologies. The respiratory flow rate and lung volume information were used to group the SCG events into inspiratory/expiratory groups or low/high lung-volume groups, respectively. Although respiratory flow information could separate similar SCG events into two different groups, the lung volume information provided better grouping of similar SCGs. This suggests that variations in SCG morphology may be due, at least in part, to changes in the intrathoracic pressure or heart location since those parameters correlates more with lung volume than respiratory flow. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features, and better signal characterization, and classification.

Some pulmonary diseases and injuries are believed to correlate with lung viscoelasticity changes. Hence, a better understanding of lung viscoelastic models could provide new perspectives on the progression of lung pathology and trauma. In the presented study, stress relaxation measurements were performed to quantify relaxation behavior of pig lungs. Results have uncovered certain trends, including an initial steep decay followed by a slow asymptotic relaxation, which would be better described by a power law than exponential decay. The fractional standard linear solid (FSLS) and two integer order viscoelastic models – standard linear solid (SLS) and generalized Maxwell (GM) – were used to fit the stress relaxation curves; the FSLS was found to be a better fit. It is suggested that fractional order viscoelastic models, which have nonlocal, multi-scale attributes and exhibit power law behavior, better capture the lung parenchyma viscoelastic behavior.

Heart disease is one of leading causes of mortality worldwide. Healthy heart valves are key for proper heart function. When these valves dysfunction, a replacement is often necessary in severe cases. The current study presents an investigation of the pulsatile blood flow through a bileaflet mechanical heart valve (BMHV) where one leaflet is healthy and can fully open and the other leaflet cannot fully open with different levels of dysfunction. To better understand the implications that a dysfunctional leaflet has on the blood flow through these valves, analysis of flow characteristics such as velocity, pressure drop, wall shear stress and vorticity profiles was performed. Results suggested that leaflet dysfunction caused increased local velocities, separation regions and wall shear stresses. For example, the maximum velocity increased from 2.53 m/s to 4.9 m/s when dysfunction increased from 0% to 100%. The pressure drop increased (by up to 300%) with dysfunctionality. Results suggested that leaflet dysfunction also caused increased wall shear stresses on the valve frame where higher stresses developed around the hinges (at 75% and 100% dysfunctions). Analysis also showed that increased dysfunctionality of one leaflet led to higher net shear forces on both the healthy and dysfunctional leaflets (by up to 200% and 600%, respectively)., 6 pages, 7 figures, 1 table, Journal of Applied Biotechnology & Bioengineering

Physicians are not evenly distributed throughout the United States. To overcome this physical limitation, we propose a concept of medical expertise pooling, which will redistribute the expertise throughout the nation more evenly. To establish this concept, we are proposing a system with emerging tele sensing and manipulation technologies over the network. In case of emergency, the proposed system can particularly be more important as it will provide more efficient support in the critical battle against time for diagnosis and treatment of a patient. Despite the physical constraint of long distance between the physician and the patient, the system will provide the visual, body sound and tactile feedback to ensure timely diagnosis and treatment that can contribute to increase in survival rates. While the proposed system utilizes existing technologies and resources, it is highly innovative as it implements a medical expert pooling paradigm that can lead to significant advancements in patient care.

Background: A method for generating bronchial tree would be helpful when constructing models of the tree for benchtop experiments as well as for numerical modeling of flow or sound propagation in the airways. Early studies documented the geometric details of the human airways that were used to develop methods for generating human airway tree. However, methods for generating animal airway tree are scarcer. Earlier studies suggested that the morphology of animal airways can be significantly different from that of humans. Hence, using algorithms for the human airways may not be accurate in generating models of animal airway geometry. Objective: The objective of this study is to develop an algorithm for generating pig airway tree based on the geometric details extracted from the physical measurements. Methods: In the current study, measured values of branch diameters, lengths and bifurcation angles and rotation of bifurcating planes were used to develop an algorithm that is capable of generating a realistic pig airway tree. Results: The generation relations between parent and daughter branches were found to follow certain trends. The diameters and the length of different branches were dependent on airway generations while the bifurcation angles were primarily dependent on bifurcation plane rotations. These relations were sufficient to develop rules for generating a model of the pig large airways. Conclusion: The results suggested that the airway tree generated from the algorithm can provide an approximate geometric model of pig airways for computational and benchtop studies.

Chest physical examination often includes performing chest percussion, which involves introducing sound stimulus to the chest wall and detecting an audible change. This approach relies on observations that underlying acoustic transmission, coupling, and resonance patterns can be altered by chest structure changes due to pathologies. More accurate detection and quantification of these acoustic alterations may provide further useful diagnostic information. To elucidate the physical processes involved, a realistic computer model of sound transmission in the chest is helpful. In the present study, a computational model was developed and validated by comparing its predictions with results from animal and human experiments which involved applying acoustic excitation to the anterior chest, while detecting skin vibrations at the posterior chest. To investigate the effect of pathology on sound transmission, the computational model was used to simulate the effects of pneumothorax on sounds introduced at the anterior chest and detected at the posterior. Model predictions and experimental results showed similar trends. The model also predicted wave patterns inside the chest, which may be used to assess results of elastography measurements. Future animal and human tests may expand the predictive power of the model to include acoustic behavior for a wider range of pulmonary conditions.

Introduction Seismocardiography (SCG) is a potential method for ambulatory assessment of heart failure (HF) status. New sensor and digital processing capabilities are expanding the possible utility of this approach. The SCG waveform, however, exhibits significant variability to which respiratory effects may be a major contributor. Hence, accounting for respiratory effects may improve SCG waveform interpretation, and support improved utility for HF assessment. Objective The objective of this study was to document SCG waveform respiratory variation and determine optimal criteria for minimizing its impact on signal variability. Methods Air flow and SCG were simultaneously measured in 20 healthy subjects. SCG waveforms were divided into two groups using three different criteria: Inspiration vs. expiration, low vs. high lung volume, and maximum intra-group similarity using Euclidean distance within and between groups. Results The separation between the two groups significantly improved when the maximum intra-group similarity criterion was implemented (p Conclusions Optimal decrease in signal variability was achieved using a maximum intra-group similarity method. Use of this may help increase robustness of SCG signal interpretation and their utility for HF monitoring.

Accurate knowledge of the airway geometry is needed when constructing physical models of the airway tree and for numerical modeling of flow or sound propagation in the airways. Human and animal experiments are conducted to validate these models. Many studies documented the geometric details of the human airways. However, information about the geometry of pig airways is scarcer. Earlier studies suggested that the morphology of animal airways can be significantly different from that of humans. The objective of this study is to measure the airway diameter, length and bifurcation angles in domestic pigs using computed tomography. In this study, lungs of six pigs were imaged, then segmentation software tools were used to extract the geometry of the airway lumen. The airway dimensions were measured from the resulting 3‐D models for the first 24 airway generations. Results showed that the size and morphology of the airways of the six pigs were similar. The trachea diameters were found to be comparable to the typical human adult, but the diameter, length and branching angles of other airways were noticeably different from that of humans. For example, pig airways consistently had an early branching from the trachea that feeds the top right lung lobe and precedes the main carina. This branch is absent in the human airways. The results suggested that the pig airways geometry may not be accurately approximated by human airways and this approximation may contribute to increasing the errors in computational models of the pig chest.