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Principal Investigator: Behnood Gholami
Organization: AUTONOMOUS HEALTHCARE, INC.
Fiscal Year: 2024
Award: $54,997
Funding agency: National Institute on Drug Abuse
The US opioid crisis continues to have a catastrophic impact on human lives and the ongoing COVID-19
pandemic is compounding its effects. Based on the statistics published by the CDC, 91,799 drug overdose
deaths occurred in the US in 2020, where the age-adjusted overdose deaths increased by 31% from 2019 to
2020. In addition, opioids, which cause respiratory depression, were involved in 75% of all drug overdose
deaths in the US. We propose to build on our work in non-invasive monitoring of vital signs to develop an FDA-
regulated medical device with a primary application in monitoring patients for opioid-induced respiratory
depression. This includes at-home monitoring of patients with chronic pain being treated with high-dose opioid
prescription medications or patients suffering from opioid use disorder (OUD) as well as monitoring subjects
with OUD at supervised injection sites (also known as supervised consumption spaces). Our overall goal is to
develop a non-contact multi-modal monitoring system for the detection of opioid-induced respiratory
depression at home and in supervised injection sites. While radar is capable of penetrating through clothing
and blankets to measure chest wall movements resulting from respiration, it requires the guidance of depth
imaging to target a person and the chest area. Our specific aims are: 1. Estimate tidal volume using a
noncontact monitoring system. Our current technology is capable of detecting respiratory rate with a high
degree of accuracy for stationary subjects. However, robust detection of respiratory depression involves
monitoring of respiratory rate, pattern, and depth (i.e., tidal volume). As part of this specific aim, we will develop
a framework to estimate tidal volume of a stationary subject using radar and depth information, where we
estimate tidal volume from chest wall displacements. Furthermore, we will extract features to characterize
respiratory pattern from the acquired radar signal. As a primary validation of this estimation framework, our
system will be tested on 20 healthy volunteers. The outcome of the test will provide us with preliminary data
regarding the accuracy of the radar and the depth-based tidal volume estimation as compared with the gold
standard. 2. Develop and validate a framework for integrating data from sensors to detect respiratory
depression. In this specific aim, we will develop a framework to use the respiratory rate, respiratory pattern,
and tidal volume information from the radar and depth camera to determine if respiratory depression has
occurred. This involves a two-step approach, where we extract respiratory features to characterize respiratory
patterns to complement respiratory rate and tidal volume, and then use a machine learning model to detect the
occurrence of respiratory depression. To help with design the right model, we will collect data using our radar
and depth imaging system from anesthetized pigs going through opioid-induced respiratory depression.
Terms: <Age><Anesthesia><Anesthesia procedures><Area><COVID crisis><COVID epidemic><COVID pandemic><COVID-19 crisis><COVID-19 epidemic><COVID-19 era><COVID-19 global health crisis><COVID-19 global pandemic><COVID-19 health crisis><COVID-19 pandemic><COVID-19 period><COVID-19 public health crisis><COVID-19 years><Cell Communication and Signaling><Cell Signaling><Chest><Chest Wall><Chest wall structure><Clothing><Complement><Complement Proteins><Consumption><Data><Detection><Dose><Drug Prescribing><Drug Prescriptions><Drugs><Family suidae><Goals><Home><Human><Image><Injections><Intracellular Communication and Signaling><Measures><Medical Device><Medication><Modeling><Modern Man><Monitor><Movement><Opiates><Opioid><Outcome><Patient Monitoring><Patients><Pattern><Penetration><Persons><Pharmaceutical Preparations><Pigs><Publishing><Radar><Respiration><Respiratory Depression><SARS-CoV-2 epidemic><SARS-CoV-2 global health crisis><SARS-CoV-2 global pandemic><SARS-CoV-2 pandemic><SARS-coronavirus-2 epidemic><SARS-coronavirus-2 pandemic><Severe Acute Respiratory Syndrome CoV 2 epidemic><Severe Acute Respiratory Syndrome CoV 2 pandemic><Severe acute respiratory syndrome coronavirus 2 epidemic><Severe acute respiratory syndrome coronavirus 2 pandemic><Signal Transduction><Signal Transduction Systems><Signaling><Site><Suidae><Swine><System><Technology><Testing><Thorace><Thoracic><Thoracic Wall><Thorax><Tidal Volume><Validation><Ventilatory Depression><Work><ages><biological signal transduction><body movement><chronic pain patient><complementation><coronavirus disease 2019 crisis><coronavirus disease 2019 epidemic><coronavirus disease 2019 global health crisis><coronavirus disease 2019 global pandemic><coronavirus disease 2019 health crisis><coronavirus disease 2019 pandemic><coronavirus disease 2019 public health crisis><coronavirus disease crisis><coronavirus disease epidemic><coronavirus disease pandemic><coronavirus disease-19 global pandemic><coronavirus disease-19 pandemic><data integration><depressed breathing><depression of breathing><design><designing><detection platform><detection system><drug/agent><healthy volunteer><homes><imaging><imaging system><licit opioid><machine learning based model><machine learning model><medication prescription><multi-modality><multimodality><non-invasive monitor><noninvasive monitor><opiate crisis><opiate medication><opiate use disorder><opioid crisis><opioid epidemic><opioid medication><opioid use disorder><overdose death><overdose fatalities><patient with chronic pain><porcine><prescribed medication><prescribed opiate><prescribed opioid><prescription opiate><prescription opioid><remote monitoring><respiratory><respiratory airway volume><respiratory mechanism><sensor><severe acute respiratory syndrome coronavirus 2 global health crisis><severe acute respiratory syndrome coronavirus 2 global pandemic><statistics><suid><validations>