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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.

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.

The measurement of chest surface vibrations resulting from cardiac activity is known as seismocardiography (SCG). These signals, typically measured using accelerometers on the chest surface, appear to be primarily generated by myocardial muscle activity, blood momentum changes, and heart valve movements. SCG signals contain important features associated with cardiac mechanical activity, which can be used for monitoring/predicting cardiac health. However, SCG signals are known to vary with chest surface location, which can confound diagnostic interpretation of SCG longitudinal changes over time. Documenting spatial changes will assist with both the identification of optimal sensor placement locations and the impact of misplacements on SCG interpretation. The purpose of this study is to explore the effect of sensor placement on diagnostically important SCG features and to identify locations on the anterior chest surface with consistently strong signal strength and high signal-to-noise ratio. In the current study, the SCG signals were acquired in 15 healthy male subjects along with ECG and respiratory flow measurements for 5–10 min. Here, SCG signals were acquired using an array of 32 sensors placed on the chest surface between the right and left anterior axillary and between the 2nd and 5th intercostal spaces. In addition, four accelerometers were placed at right and left mid-clavicle, mid-sternum, and xiphoid process. SCG waveforms were segmented into SCG beats (i.e., heart cycles) using the ECG R wave. Prior to extracting features, SCG beats were clustered into two groups using unsupervised machine learning to help reduce the effects of the respiratory SCG variations in order to attain more precise feature extraction. Several SCG features including SCG signal morphological variability, amplitude variation, signal-to-noise ratio, cardiac timing intervals, and other features were calculated, and the spatial variability of these features were analyzed. Results suggested that the magnitude of acceleration and signal-to-noise ratio were consistently higher (50~80%) around a 3 cm wide region lateral to the left lower sternal border ranging from the 3rd to 5th intercostal space (ICS) as well as over the xiphoid process. The SCG signal morphological variability values were approximately 4–5% of the signal peak to peak amplitude in this region. Cardiac timing intervals (pre-ejection period and left ventricle ejection period) were also comparable (within 2–5%) in this region. Spatial variations of other potentially useful SCG features (including maximum instantaneous frequency and S3 amplitude) were small (within 5%) at the left lower sternal border and at the xiphoid process. The optimum sensor locations with maximum signal strength and lowest spatial variation of SCG features were found to be at the left lower sternal border ranging from 3rd to 5th ICS and at the xiphoid process. Documentation of these changes should help future researchers and clinicians optimize SCG sensor placement.

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].

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.

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 (~

Introduction Recent advances in sensor technology, digital processing and artificial intelligence can be used to potentially increase the utility of seismocardiography (SCG) for ambulatory assessment of heart failure (HF) patients. SCG respiratory variability, energy spectrum shifts to ultra-low frequencies, third heart sound amplitude and other features may provide early warning of HF deterioration. If these non-invasively detected changes prove to be reliable, then earlier intervention may reduce readmissions, costs, and perhaps morbidity and mortality. Objective The objective of this study was to investigate early SCG waveform features that may be predictive of HF re-admission. Methods SCG was longitudinally followed in 40 HF patients, 9 (23%) of whom were readmitted during the study period (median follow-up 1.5 mos). SCG waveforms were clustered into two groups (that correlate with respiration) using unsupervised machine learning. Following this, various signal features were calculated using digital signal processing including waveform respiratory variability, sub-audible spectral energy distribution, and SCG3 (“S3”) amplitude. Results Similar to prior observations with heart rate variability, the intra group SCG variability tended to decrease as the patient approached re-admission (table below). The energy ratio between the ultra-low frequency band (0.5-5 Hz) and the broader sub-audible frequency band (0.5-15 Hz) showed a shift towards the lower frequencies as the subjects approached re-admission. In addition, SCG3 amplitude (which corresponds to S3) appeared to increase as the subjects approached readmission. Conclusions We report preliminary data showing some SCG features that appeared to be early predictors of HF readmission. Features included Intra-group variability, along with the SCG downward frequency shifts and increased SCG3 amplitude Ongoing work will help to better characterize and validate these initial observations

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.

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.

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.

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.

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.

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.

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.

Breath sounds are often used to aid in the diagnosis of pulmonary disease. Mechanical and numerical models could be used to enhance our understanding of relevant sound transmission phenomena. Sound transmission in an airway mimicking phantom was investigated using a mechanical model with a branching airway network embedded in a compliant viscoelastic medium. The Horsfield self-consistent model for the bronchial tree was adopted to topologically couple the individual airway segments into the branching airway network. The acoustics of the bifurcating airway segments were measured by microphones and calculated analytically. Airway phantom surface motion was measured using scanning laser Doppler vibrometry. Finite element simulations of sound transmission in the airway phantom were performed. Good agreement was achieved between experiments and simulations. The validated computational approach can provide insight into sound transmission simulations in real lungs.

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.

Contact transducers are a key element in experiments involving body sounds. The characteristics of these devices are often not known with accuracy. There are no standardized calibration setups or procedures for testing these sensors. This study investigated the characteristics of a new computer-controlled sound source phantom for testing sensors. Results suggested that sensors with different sizes require special phantom requirements. The effectiveness of certain approaches on increasing the spatial and spectral uniformity of the phantom surface signal was studied. Non-uniformities >20 dB were removable, which can be particularly helpful in comparing the characteristics of different size sensors more accurately.

Introduction Many strategies and technologies have been developed to minimize readmissions and manage HF. These strategies are often either labor intensive, invasive and/or expensive. New methodologies for accurate, rapid and low-cost ambulatory metrics of HF status are needed, of which seismocardiography (SCG) may be particularly useful. SCG uses accelerometer sensors to detect both audible, and sub-audible (i.e., below the frequencies detectable by the human ear) vibration signals. Although described decades ago, new sensor and digital processing capabilities are expanding the possible utility of this approach. Objective To investigate the use of SCG for HF monitoring. Methods Twenty patients (ages 51 to 75, 30 % female) admitted for HF were studied with a contact SCG sensor on the left lower sternal border of the chest wall at the 4thintercostal space for 1 min at two points in time with an interval of 2 to 48 hrs between assessments. The collected data contained 34 SCG waveform pairs: 17 pairs for patients with a NYHA class change from IV to III and 17 pairs for patients who stayed in class III. Results SCG signal waveform time-amplitude features were analyzed and the waveform variation quantified between the recording pairs. Results showed that the waveform variation was significantly different for those w/ and w/o NYHA class change (p Conclusions SCG changes may be a useful, safe and inexpensive modality for outpatient HF monitoring, enabling more timely intervention. These preliminary findings are supportive of an ability to differentiate HF functional status change. Further prospective studies are warranted.

© Mary Ann Liebert, Inc. February 2009 Obesity Management 29 The Expert Panel recommended four stages of treatment for overweight children (and their families).1 They used the principle of least intervention, starting slowly with a basic educational approach and then adding stages (steps) if necessary to achieve substantial improvement. That step-wise method is sensible and clear. However, we believe an expanded set of steps, from four to seven, will prove more useful, making it even clearer to parents of overweight children and teens what they can do to help their children. The seven steps defined below all make one very important assumption: pediatric obesity is a family affair. Parents who want their children to succeed must become fully involved, knowledgeable, and participate very actively in major lifestyle changes for themselves as well as their children. The illustration, “Seven Steps to Success,” can serve as a useful handout for the families with whom you work, summarizing seven levels of intensity of intervention. As shown in the illustration, each step could lead to favorable outcomes (notice the directional arrow pointing to “Health and Wellness” from each step), for some families some of the time. For example, by providing explicit feedback about BMI percentile and category (e.g., 97%–obese), clear suggestions for changing diet and activities, and appropriate parent guidebooks, some highly motivated families may be able to make substantial lifestyle changes and produce significant weight loss in their overweight children. However, most families with obese children or teens will find it necessary to go from the first step up to the second and often higher, adding levels of intensity to get the knowledge, skills, and support necessary to master this challenging problem. You will help your families with overweight children tremendously by doing what you can to promote an attitude of persistence. One, two, or even five steps may not produce healthier weights (and modified lifestyles), but you can make the following points

Mechanical models of soft tissue are useful for studying vibro-acoustic phenomena. They may be used for validating mathematical models and for testing new equipment and techniques. The objective of this study was to measure density and visco-elastic properties of synthetic materials that can be used to build such models. Samples of nine different materials were tested under dynamic (0.5 Hz) compressive loading conditions. The modulus of elasticity of the materials was varied, whenever possible, by adding a softener during manufacturing. The modulus was measured over a nine month period to quantify the effect of ageing and softener loss on material properties. Results showed that a wide range of the compression elasticity modulus (10 to 1400 kPa) and phase (3.5 degrees -16.7 degrees ) between stress and strain were possible. Some materials tended to exude softener over time, resulting in a weight loss and elastic properties change. While the weight loss under normal conditions was minimal in all materials (

Many pulmonary injuries and pathologies may lead to structural and functional changes in the lungs resulting in measurable sound transmission changes on the chest surface. Additionally, noninvasive imaging of externally driven mechanical wave motion in the chest (e.g., using magnetic resonance elastography) can provide information about lung structural property changes and, hence, 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, lung, and chest wall under normal and pneumothorax conditions. Experiments were carried out to validate the model. Here, sound waves with frequency content from 50 to 700 Hz were introduced into airways of five porcine subjects via an endotracheal tube, and transmitted waves were measured by scanning laser Doppler vibrometry at the chest wall surface. The computational model predictions of decreased sound transmission with pneumothorax were consistent with experimental measurements. The in silico model can also be used to visualize wave propagation inside and on the chest wall surface for other pulmonary pathologies, which may help in developing and interpreting diagnostic procedures that utilize sound and vibration.

Endotracheal tube (ETT) malpositioning into a mainstem bronchus or the esophagus may result in significant hypoxemia. Current methods to determine correct ETT position include auscultation, radiography, and bronchoscopy, although the current acceptable standard procedure for proper endotracheal (versus esophageal) intubation is detection of end-tidal carbon dioxide (ETco(2)) by capnography, capnometry, or colorimetric ETco(2) devices. Unfortunately, capnography may be unavailable or unreliable in nonhospital/emergency settings or in low cardiac output states, and it does not detect endobronchial intubation. The purpose of this study was to quantify and assess breath sound characteristics using electronic stethoscopes placed over each hemithorax and epigastrium to determine their ability to detect ETT malposition. We recorded breath sounds in 19 healthy, non-obese adults before general surgical procedures. After intubation of the trachea, the ETT was bronchoscopically positioned 3 cm above the carina, after which 3 breaths of 500 mL were given and breath sounds were recorded. A second ETT was placed in the esophagus and the same series of breaths and recordings were performed. Finally, the tracheal ETT was advanced into the right mainstem bronchus and breath sounds were recorded. Using computerized analysis, breath sounds were digitized and filtered to remove selected frequencies, and acoustic signals and energy ratios were obtained for all 3 positions. Total energy ratios using band-pass filtering of the acoustic signals accurately identified all esophageal and endobronchial intubation (P < 0.001). These preliminary results suggest that this technique, when incorporated into a 3-component, electronic stethoscope-type device, may be an accurate, portable mechanism to reliably detect ETT malposition in adults when ETco(2) may be unavailable or unreliable.

Pneumothorax (PTX) is an abnormal accumulation of air between the lung and the chest wall. It is a relatively common and potentially life-threatening condition encountered in patients who are critically ill or have experienced trauma. Auscultatory signs of PTX include decreased breath sounds during the physical examination. The objective of this exploratory study was to investigate the changes in sound transmission in the thorax due to PTX in humans. Nineteen human subjects who underwent video-assisted thoracic surgery, during which lung collapse is a normal part of the surgery, participated in the study. After subjects were intubated and mechanically ventilated, sounds were introduced into their airways via an endotracheal tube. Sounds were then measured over the chest surface before and after lung collapse. PTX caused small changes in acoustic transmission for frequencies below 400 Hz. A larger decrease in sound transmission was observed from 400 to 600 Hz, possibly due to the stronger acoustic transmission blocking of the pleural air. At frequencies above 1 kHz, the sound waves became weaker and so did their changes with PTX. The study elucidated some of the possible mechanisms of sound propagation changes with PTX. Sound transmission measurement was able to distinguish between baseline and PTX states in this small patient group. Future studies are needed to evaluate this technique in a wider population.