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Estimating visceral adiposity from wrist-worn accelerometry

Summary

Visceral adipose tissue (VAT) is a key marker of both metabolic health and habitual physical activity (PA). Excess VAT is highly correlated with type 2 diabetes and insulin resistance. The mechanistic basis for this pathophysiology relates to overloading the liver with fatty acids. VAT is also a highly labile fat depot, with increased turnover stimulated by catecholamines during exercise. VAT can be measured with sophisticated imaging technologies, but can also be inferred directly from PA.We tested this relationship using National Health and Nutrition Examination Survey (NHANES) data from 2011-2014, for individuals aged 20-60 years with 7 days of accelerometry data (n=2,456 men; 2,427 women) [1]. Two approaches were used for estimating VAT from activity. The first used engineered features based on movements during gait and sleep, and then ridge regression to map summary statistics of these features into a VAT estimate. The second approach used deep neural networks trained on 24 hours of continuous accelerometry. A foundation model first mapped each 10 s frame into a high-dimensional feature vector. A transformer model then mapped each day's feature vector time series into a VAT estimate, which were averaged over multiple days. For both approaches, the most accurate estimates were obtained with the addition of covariate information about subject demographics and body measurements. The best performance was obtained by combining the two approaches, resulting in VAT estimates with correlations of r=0.86. These findings demonstrate a strong relationship between PA and VAT and, by extension, between PA and metabolic health risks.
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Summary

Visceral adipose tissue (VAT) is a key marker of both metabolic health and habitual physical activity (PA). Excess VAT is highly correlated with type 2 diabetes and insulin resistance. The mechanistic basis for this pathophysiology relates to overloading the liver with fatty acids. VAT is also a highly labile fat...

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Monitoring of hemorrhage and whole blood resuscitation in non-human primates

Summary

Massive hemorrhage remains the primary cause of potentially preventable death in traumatic injuries. Monitoring hemorrhage and resuscitation accurately can improve outcomes but continues to be challenging since traditional vital signs are highly compensated by the body. Previous work has developed physiologically interpretable algorithms to assess volume status in simulated models of hemorrhage and resuscitation. In this paper, we further develop these algorithms to assess volume status in a nonhuman primate model of controlled blood loss and subsequent whole-blood resuscitation. We acquired arterial blood pressure (ABP) waveform data on 12 adult male baboons during a step-and-hold protocol for hemorrhage and a constant resuscitation rate. A gradient-boosted regression tree model trained on only the ejected-wave pulse area (EWPA) feature yielded a 19% root-mean-square-error (RMSE), 0.71 R2, and an area under the receiver operating characteristic curve of ≥ 0.9 for key operating points of volume status. The performance of this model with a single feature compares well to results reported previously from single-feature machine-learning (ML) models as well as more complex machine learning models that are difficult to interpret and computationally intensive. This study is the first investigation of these physiologically interpretable models on invasively measured ABP waveforms.
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Summary

Massive hemorrhage remains the primary cause of potentially preventable death in traumatic injuries. Monitoring hemorrhage and resuscitation accurately can improve outcomes but continues to be challenging since traditional vital signs are highly compensated by the body. Previous work has developed physiologically interpretable algorithms to assess volume status in simulated models...

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Endovascular localization of aortic injury in a porcine model

Summary

Goal: Non-compressible torso hemorrhage represents a category of lethal injuries in both civilian and military traumatically injured populations that with proper intervention, training, or technological advancements are survivable. Endovascular localization of active bleeding in the pre-hospital setting can allow faster, less invasive, and more accurate applications of life-saving interventions. In this paper, we report initial in vivo and in silico experimental results to test the feasibility of endovascular localization of hemorrhage. Methods: Endovascular pressure waveforms were acquired on five pigs with an induced aortic injury via a custom intra-aortic catheter instrumented with four pressure sensors. Pressure and velocity data were then simulated on an in silico human aortic model with the same kind of injury. Results: A decrease in pulse pressure across the injury (proximal to distal) reliably indicated the injury location to within a few centimeters. The simulated model showed a similar decrease in pulse pressure as well as an increase in velocity. Conclusions: With additional refinement, localization accuracy may be sufficient for application of a modern covered stent to stop bleeding. The simulated model results indicate relevance for humans and provide guidance for future experiments.
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Summary

Goal: Non-compressible torso hemorrhage represents a category of lethal injuries in both civilian and military traumatically injured populations that with proper intervention, training, or technological advancements are survivable. Endovascular localization of active bleeding in the pre-hospital setting can allow faster, less invasive, and more accurate applications of life-saving interventions. In...

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Variability of speech timing features across repeated recordings: a comparison of open-source extraction techniques

Summary

Variations in speech timing features have been reliably linked to symptoms of various health conditions, demonstrating clinical potential. However, replication challenges hinder their
translation; extracted speech features are susceptible to methodological variations in the recording and processing pipeline. Investigating this, we compared exemplar timing features extracted via three different techniques from recordings of healthy speech. Our results show that features extracted via an intensity-based method differ from those produced by forced alignment. Different extraction methods also led to differing estimates of within-speaker feature variability over time in an analysis of recordings repeated systematically over three sessions in one day (n=26) and in one week (n=28). Our findings highlight the importance of feature extraction in study design and interpretation, and the need for consistent, accurate extraction techniques for clinical research.
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Summary

Variations in speech timing features have been reliably linked to symptoms of various health conditions, demonstrating clinical potential. However, replication challenges hinder their
translation; extracted speech features are susceptible to methodological variations in the recording and processing pipeline. Investigating this, we compared exemplar timing features extracted via three different techniques...

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Building digital twins for cardiovascular health: From principles to clinical impact

Summary

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.
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Summary

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2)...

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Microbubble contrast agents improve detection of active hemorrhage

Published in:
IEEE Open Journal of Engineering in Medicine and Biology, doi: 10.1109/OJEMB.2024.3414974

Summary

Assessment of trauma-induced hemorrhage with ultrasound is particularly challenging outside of the clinic, where its detection is crucial. The current clinical standard for hematoma detection – the focused assessment with sonography of trauma (FAST) exam – does not aim to detect ongoing blood loss, and thus is unable to detect injuries of increasing severity. To enhance detection of active bleeding, we propose the use of ultrasound contrast agents (UCAs), together with a novel flow phantom and contrast-sensitive processing techniques, to facilitate efficient, practical characterization of internal bleeding. Within a the custom phantom, UCAs and processing techniques enabled a significant enhancement of the hemorrhage visualization (mean increase in generalized contrast-to-noise ratio of 17 %) compared to the contrast-free case over a range of flow rates up to 40 ml/min. Moreover, we have shown that the use of UCAs improves the probability of detection: the area under the receiver operating characteristic curve for a flow rate of 40 ml/min was 0.99, compared to 0.72 without contrast. We also demonstrate how additional processing of the spatial and temporal information further localizes the bleeding site. UCAs also enhanced Doppler signals over the non-contrast case. These results show that specialized nonlinear processing (NLP) pipelines together with UCAs may offer an efficient means to improve substantially the detection of slower hemorrhages and increase survival rates for trauma-induced injury in pre-hospital settings.
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Summary

Assessment of trauma-induced hemorrhage with ultrasound is particularly challenging outside of the clinic, where its detection is crucial. The current clinical standard for hematoma detection – the focused assessment with sonography of trauma (FAST) exam – does not aim to detect ongoing blood loss, and thus is unable to detect...

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An exploratory characterization of speech- and fine-motor coordination in verbal children with Autism spectrum disorder

Summary

Autism spectrum disorder (ASD) is a neurodevelopmental disorder often associated with difficulties in speech production and fine-motor tasks. Thus, there is a need to develop objective measures to assess and understand speech production and other fine-motor challenges in individuals with ASD. In addition, recent research suggests that difficulties with speech production and fine-motor tasks may contribute to language difficulties in ASD. In this paper, we explore the utility of an off-body recording platform, from which we administer a speech- and fine-motor protocol to verbal children with ASD and neurotypical controls. We utilize a correlation-based analysis technique to develop proxy measures of motor coordination from signals derived from recordings of speech- and fine-motor behaviors. Eigenvalues of the resulting correlation matrix are inputs to Gaussian Mixture Models to discriminate between highly-verbal children with ASD and neurotypical controls. These eigenvalues also characterize the complexity (underlying dimensionality) of representative signals of speech- and fine-motor movement dynamics, and form the feature basis to estimate scores on an expressive vocabulary measure. Based on a pilot dataset (15 ASD and 15 controls), features derived from an oral story reading task are used in discriminating between the two groups with AUCs > 0.80, and highlight lower complexity of coordination in children with ASD. Features derived from handwriting and maze tracing tasks led to AUCs of 0.86 and 0.91, however features derived from ocular tasks did not aid in discrimination between the ASD and neurotypical groups. In addition, features derived from free speech and sustained vowel tasks are strongly correlated with expressive vocabulary scores. These results indicate the promise of a correlation-based analysis in elucidating motor differences between individuals with ASD and neurotypical controls.
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Summary

Autism spectrum disorder (ASD) is a neurodevelopmental disorder often associated with difficulties in speech production and fine-motor tasks. Thus, there is a need to develop objective measures to assess and understand speech production and other fine-motor challenges in individuals with ASD. In addition, recent research suggests that difficulties with speech...

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A neurophysiological-auditory "listen receipt" for communication enhancement

Published in:
49th IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 14-19 April 2024.

Summary

Information overload, and specifically auditory overload, is common in critical situations and detrimental to communication. Currently, there is no auditory equivalent of an email read receipt to know if a person has heard a message, other than waiting for a reply. This work hypothesizes that it may be possible to decode whether a person has indeed heard a message, or in other words, create an an auditory "listen receipt," through use of non-invasive physiological or neural monitoring. We extracted a variety of features derived from Electrodermal activity (EDA), Electroencephalography (EEG), and the correlations between the acoustic envelope of the radio message and EEG to use in the decoder. We were able to classify the cases in which the subject responded correctly to the question in the message, versus the cases where they missed or heard the message incorrectly, with an accuracy of 79% and a receiver operating characteristic (ROC) area under the curve (AUC) of 0.83. This work suggests that the concept of a "listen receipt" may be possible, and future wearable machine-brain interface technologies may be able to automatically determine if an important radio message has been missed for both human-to-human and human-to-machine communication.
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Summary

Information overload, and specifically auditory overload, is common in critical situations and detrimental to communication. Currently, there is no auditory equivalent of an email read receipt to know if a person has heard a message, other than waiting for a reply. This work hypothesizes that it may be possible to...

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Quantifying speech production coordination from non- and minimally-speaking individuals

Published in:
J. Autism Dev. Disord., 13 April 2024.

Summary

Purpose: Non-verbal utterances are an important tool of communication for individuals who are non- or minimally-speaking. While these utterances are typically understood by caregivers, they can be challenging to interpret by their larger community. To date, there has been little work done to detect and characterize the vocalizations produced by non- or minimally-speaking individuals. This paper aims to characterize five categories of utterances across a set of 7 non- or minimally-speaking individuals. Methods: The characterization is accomplished using a correlation structure methodology, acting as a proxy measurement for motor coordination, to localize similarities and differences to specific speech production systems. Results: We specifically find that frustrated and dysregulated utterances show similar correlation structure outputs, especially when compared to self-talk, request, and delighted utterances. We additionally witness higher complexity of coordination between articulatory and respiratory subsystems and lower complexity of coordination between laryngeal and respiratory subsystems in frustration and dysregulation as compared to self-talk, request, and delight. Finally, we observe lower complexity of coordination across all three speech subsystems in the request utterances as compared to self-talk and delight. Conclusion: The insights from this work aid in understanding of the modifications made by non- or minimally-speaking individuals to accomplish specific goals in non-verbal communication.
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Summary

Purpose: Non-verbal utterances are an important tool of communication for individuals who are non- or minimally-speaking. While these utterances are typically understood by caregivers, they can be challenging to interpret by their larger community. To date, there has been little work done to detect and characterize the vocalizations produced by...

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Optimizing MobileNet algorithms for real-time vessel detection on smartphones

Published in:
Proc. 2023 IEEE 19th Intl. Conf. on Body Sensor Networks, BSN, 9-11 October 2023.

Summary

Internal bleeding due to non-compressible torso hemorrhage is the leading cause of prehospital fatalities in civilian and military trauma. A limited number of trauma surgeons are expected to be available in disaster scenarios and future large-scale combat operations. As a result, non-specialists will need to perform life-saving interventions to address internal bleeding. A first step in mitigation is ultrasound-guided central vascular access, which involves identifying a deep blood vessel in the imagery, such as the femoral vein, femoral artery, or internal jugular vein, and then placing a needle and catheter into the vessel for follow-on resuscitation. In this paper, we demonstrate machine learning algorithms for both femoral and neck vessel detection with high accuracy and real-time speed on smartphones. The algorithms are integrated with commercial ultrasound and optimized for use on low size, weight, and power devices. Coupled with custom robotics, this technology can enable rapid vascular access by non-specialist operators using a handheld platform.
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Summary

Internal bleeding due to non-compressible torso hemorrhage is the leading cause of prehospital fatalities in civilian and military trauma. A limited number of trauma surgeons are expected to be available in disaster scenarios and future large-scale combat operations. As a result, non-specialists will need to perform life-saving interventions to address...

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