Noncontact remote monitoring for the detection of opioid-induced respiratory depression - the US opioid crisis continues to have a catastrophic impact on human lives and the ongoing covid-19…

Noncontact remote monitoring for the detection of opioid-induced respiratory depression - 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.