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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 (~

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.

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.

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.

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.

Documenting the characteristics of contact body-sound sensors is important since they are an integral part of vibroacoustic experiments. To facilitate sensor testing, a repeatable sound source is desirable. The purpose of this study was to construct a sound source phantom for testing sensors with different sizes. To facilitate accurate comparison of the characteristics of these sensors a uniform acoustic signal at the phantom surface is required. The acoustic output at the phantom surface was documented using a laser Doppler vibrometer. A rigid plate was embedded within the phantom to help achieve a uniform acoustic signal at the surface. Measurements showed that the surface acoustic output has high spatial uniformity (about ±1dB over a 6 cm diameter). The developed phantom is compact (≫ 250 ml), easy to construct, requires no special storage, and has mechanical properties similar to soft tissue.

A system was constructed to test the performance of transducers for the detection of body surface vibrations. A phantom was manufactured of a gel that approximates properties of soft tissue and the different sensors were used to measure the vibrations at the model surface. One speaker was buried in the phantom to introduce a simulated signal, and another introduced simulated ambient room noise. The frequency-dependent sensor sensitivity to the input signal and ambient noise was investigated for six different sensor types. The laser Doppler sensor provided non-contact measurement with no surface loading, and was chosen as the reference sensor. The sensitivity of all sensors to ambient noise was satisfactory with the all-coupled sensor being most sensitive. The electronic stethoscope had the advantages of high sensitivity to the desired signal, low sensitivity to ambient noise and low cost.

This study is aimed at enhancing bowel sound (or gastrointestinal acoustic phenomena-[GAP]) analysis in an anesthetized rat model. The measured signal at the rat's abdominal wall primarily contains infrequent GAP activities against a strong background of ongoing heart sounds (HS). The bandwidth of GAP events overlaps with that of HS rendering use of fixed band-pass filters inadequate. Therefore, an adaptive filter (AF) is necessary. Because no reference signal is readily available, the reference signal is generated by exploiting the low variability in HS properties. Effects of the adaptation parameter and filter order on SNR and GAP distortion due to filtering are discussed. The GAP waveform distortions were minimum for a filter dimension of 1 to 5 points and an adaptation parameter of 5 to 20% of the value at the onset of filter instability. Typical overall SNR enhancement by HS removal with the current system was 25 to 30 dB for a wide range of adaptation parameters and filter dimensions. It is concluded that an AF system can be implemented to study GAP activity in anesthetized rats.