Publications by authors named "Omer Inan"

Joint acoustic emissions (JAEs) have been used as a non-invasive sensing modality of joint health for different conditions such as acute injuries, osteoarthritis (OA), and rheumatoid arthritis (RA). Recent hardware improvements for sensing JAEs have made at-home sensing to supplement clinical visits a possibility. To complement these advances, models must be improved for JAEs to function as generalizable predictors of joint health.

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This study was undertaken to determine if knee acoustic emissions (KAE) measured at the point of care with a wearable device can classify knees with pre-radiographic osteoarthritis (pre-OA) from healthy knees. We performed a single-center cross-sectional observational study comparing KAE in healthy knees to knees with clinical symptoms compatible with knee OA that did not meet classification criteria for radiographic knee OA. KAE were measured during scripted maneuvers performed in clinic exam rooms or similarly noisy medical center locations in healthy (n=20), pre-OA (n=11), and, for comparison, OA (n=12) knees.

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Objective: This study explores the potential of active vibrational sensing as a digital biomarker to identify and characterize inflammatory symptomatology in the Achilles tendon and its entheses in juvenile idiopathic arthritis (JIA), particularly enthesitis related arthritis (ERA), a subcategory of JIA.

Methods: Active vibrational data were non-invasively recorded using a miniature coin vibration motor and accelerometer. Twenty active vibration recordings from children diagnosed with JIA were used in the analysis.

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Objective: To develop a novel synthetic multi-modal variable capable of capturing cardiovascular responses to acute mental stress and the stress-mitigating effect of transcutaneous median nerve stimulation (TMNS), as an initial step toward the overarching goal of enabling closed-loop controlled mitigation of the physiological response to acute mental stress.

Methods: Using data collected from 40 experiments in 20 participants involving acute mental stress and TMNS, we examined the ability of six plausibly explainable physio-markers to capture cardiovascular responses to acute mental stress and TMNS. Then, we developed a novel synthetic multi-modal variable by fusing the six physio-markers based on numerical optimization and compared its ability to capture cardiovascular responses to acute mental stress and TMNS against the six physio-markers in isolation.

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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.

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Body motion tracking for medical applications has the potential to improve quality of life for people with physical or speech motor disorders. Current solutions available in the market are either inaccurate, not affordable, or are impractical for a medical setting or at home. Magnetic localization can address these issues thanks to its high accuracy, simplicity of use, wearability, and use of inexpensive sensors such as magnetometers.

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Background: Posttraumatic stress disorder (PTSD) causes heightened fight-or-flight responses to traumatic memories (i.e., hyperarousal).

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Article Synopsis
  • * A deep learning model utilizing the U-Net architecture has been created to effectively clean SCG signals affected by walking-induced noise, showing a 90% similarity improvement to clean signals.
  • * After applying the model, heart rate estimation accuracy showed significant improvements, with mean absolute errors dropping to 1.21 BPM and RMSEs for aortic timing estimations also decreasing substantially, indicating better performance in health monitoring during daily activities.
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Objective: Real-time measurement of biological joint moment could enhance clinical assessments and generalize exoskeleton control. Accessing joint moments outside clinical and laboratory settings requires harnessing non-invasive wearable sensor data for indirect estimation. Previous approaches have been primarily validated during cyclic tasks, such as walking, but these methods are likely limited when translating to non-cyclic tasks where the mapping from kinematics to moments is not unique.

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Sepsis is a major public health emergency and one of the leading causes of morbidity and mortality in critically ill patients. For each hour treatment is delayed, shock-related mortality increases, so early diagnosis and intervention is of utmost importance. However, earlier recognition of shock requires active monitoring, which may be delayed due to subclinical manifestations of the disease at the early phase of onset.

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Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective.

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Tasks of daily living are often sporadic, highly variable, and asymmetric. Analyzing these real-world non-cyclic activities is integral for expanding the applicability of exoskeletons, protheses, wearable sensing, and activity classification to real life, and could provide new insights into human biomechanics. Yet, currently available biomechanics datasets focus on either highly consistent, continuous, and symmetric activities, such as walking and running, or only a single specific non-cyclic task.

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Article Synopsis
  • Patients with previous heart attacks (MIs) face higher risks of future events, especially when stressed, prompting the need for effective monitoring tools.
  • A new miniaturized patch designed to record multiple heart signals (ECG, SCG, PPG) has shown promising results in matching key ECG-derived features with those from a standard device (Biopac) during stress tests, indicating its potential reliability.
  • The strong correlation in data (e.g., heart rate and variability) suggests that this patch could be clinically beneficial for monitoring post-MI patients and enhancing their outcomes, pending further evaluation.
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Millions around the world suffer from traumatic stress (stress caused by traumatic memories). Transcutaneous cervical vagus nerve stimulation (tcVNS) has been shown to counteract physiological changes associated with traumatic stress. However, little is known regarding the approximate timecourse of tcVNS effects.

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Background: Opioid Use Disorder (OUD) is an escalating public health problem with over 100,000 drug overdose-related deaths last year most of them related to opioid overdose, yet treatment options remain limited. Non-invasive Vagal Nerve Stimulation (nVNS) can be delivered via the ear or the neck and is a non-medication alternative to treatment of opioid withdrawal and OUD with potentially widespread applications.

Methods: This paper reviews the neurobiology of opioid withdrawal and OUD and the emerging literature of nVNS for the application of OUD.

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Post-traumatic stress disorder (PTSD) is an independent risk factor for developing heart failure; however, the underlying cardiac mechanisms are still elusive. This study aims to evaluate the real-time effects of experimentally induced PTSD symptom activation on various cardiac contractility and autonomic measures. We recorded synchronized electrocardiogram and impedance cardiogram from 137 male veterans (17 PTSD, 120 non-PTSD; 48 twin pairs, 41 unpaired singles) during a laboratory-based traumatic reminder stressor.

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Article Synopsis
  • Researchers used wearable sensors to monitor heart rate and movement in US Army Rangers and Combat Engineers during intense marches, aiming to predict exertional heat stroke (EHS) early on.
  • Data from 478 participants were analyzed using machine learning to assess physical strain and stress, successfully predicting EHS up to 69 minutes before it occurred in three cases.
  • The study suggests that this predictive method can be adapted to other activities and improved with new sensor technology, potentially aiding in health intervention strategies.
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Chronic respiratory diseases affect millions and are leading causes of death in the US and worldwide. Pulmonary auscultation provides clinicians with critical respiratory health information through the study of Lung Sounds (LS) and the context of the breathing-phase and chest location in which they are measured. Existing auscultation technologies, however, do not enable the simultaneous measurement of this context, thereby potentially limiting computerized LS analysis.

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Stress is a major determinant of health and wellbeing. Conventional stress management approaches do not account for the daily-living acute changes in stress that affect quality of life. The combination of physiological monitoring and non-invasive Peripheral Nerve Stimulation (PNS) represents a promising technological approach to quantify stress-induced physiological manifestations and reduce stress during everyday life.

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Article Synopsis
  • Transcutaneous cervical vagus nerve stimulation (tcVNS) is explored as a treatment for stress-related psychiatric disorders, with ghrelin identified as a possible stress biomarker.
  • A randomized double-blind study found that tcVNS lowers ghrelin levels significantly during and after stress-inducing tasks compared to a sham treatment.
  • The study suggests that tcVNS helps regulate hormonal responses to stress, although there are limitations such as measurement timing and the stimulation focused only on the left vagus nerve.
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Objective: To develop a novel physical model-based approach to enable 1-point calibration of pulse transit time (PTT) to blood pressure (BP).

Methods: The proposed PTT-BP calibration model is derived by combining the Bramwell-Hill equation and a phenomenological model of the arterial compliance (AC) curve. By imposing a physiologically plausible constraint on the skewness of AC at positive and negative transmural pressures, the number of tunable parameters in the PTT-BP calibration model reduces to 1.

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Background: Posttraumatic stress disorder (PTSD) is associated with changes in multiple neurophysiological systems, including verbal declarative memory deficits. Vagus Nerve Stimulation (VNS) has been shown in preliminary studies to enhance function when paired with cognitive and motor tasks. The purpose of this study was to analyze the effect of transcutaneous cervical VNS (tcVNS) on attention, declarative and working memory in PTSD patients.

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Objective: Muscle health and decreased muscle performance (fatigue) quantification has proven to be an invaluable tool for both athletic performance assessment and injury prevention. However, existing methods estimating muscle fatigue are infeasible for everyday use. Wearable technologies are feasible for everyday use and can enable discovery of digital biomarkers of muscle fatigue.

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Background: Joint acoustic emissions from knees have been evaluated as a convenient, non-invasive digital biomarker of inflammatory knee involvement in a small cohort of children with Juvenile Idiopathic Arthritis (JIA). The objective of the present study was to validate this in a larger cohort.

Findings: A total of 116 subjects (86 JIA and 30 healthy controls) participated in this study.

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Article Synopsis
  • Wearable systems can evaluate cardiovascular health in real-time by estimating hemodynamic indices using seismocardiograms (SCG) that capture cardiac activity like the opening and closing of the aortic valve.
  • Current methods struggle with accuracy due to physiological variability and noise, prompting the development of an adaptable Gaussian Mixture Model (GMM) to track multiple related cardiac features.
  • Testing showed that this model has low latency and high accuracy, making it effective for real-time monitoring and applicable in various healthcare scenarios, especially in emergency settings.
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