Acoustic OSA Prediction During Wakefulness and Monitoring During Sleep

Overview

The purpose of this study is to investigate the feasibility of sound analysis for: a) sleep apnea detection both during wakefulness and sleep, and b) flow-sound relationship during both wakefulness and sleep in patients and control individuals. The ultimate goal of our research is to simplify the current assessments for sleep apnea detection so that it is more convenient for patients and also much faster than the current techniques.

Sleep apnea, in particular obstructive sleep apnea (OSA), is one of the most common breathing disorders and is associated with major comorbidities such as higher risk perioperative complications. This is particularly concerning given that about 40-80% of people with moderate-severe OSA remain undiagnosed. Due to the resource-intensive assessments required to diagnose OSA and the significantly increased risk of car accidents and perioperative complications associated with undiagnosed OSA, there is a critical need to develop a more effective method to screen for OSA quickly and reliably. The most widely used clinical OSA screening tool is the STOP-Bang questionnaire, which is a quick and easy-to-implement inquiry form that has a high sensitivity to detect moderate and severe OSA (\>93%), but it has a very high rate of false positives (\>63%). Thus, a significant number of patients without OSA will continue to be referred for PSG, which contributes to a strain on the healthcare system. Therefore, a quick and reliable screening tool for OSA and its severity during wakefulness is very appealing but challenging, as people with OSA do not show any apparent symptoms during wakefulness. We have developed a novel screening algorithm for OSA based on the analysis of tracheal breathing sounds recorded from an individual during wakefulness, called AWakeOSA. It can predict OSA with a sensitivity (86%) similar to STOP-Bang, but with a much higher specificity (84%) for detecting individuals without OSA. The AWakeOSA technology still needs significant research and quality improvements to become a reliable home-care device for screening under unsupervised conditions, which is the central purpose of this project. In addition, we are interested to investigate the breathing sound changes from wakefulness to sleep in both groups of healthy and apneic population. For that, we need to record PSG data and breathing sounds during sleep in addition to recording breathing sounds during wakefulness. We have also designed a specialized hardware device, called ASAD-3, capable of recording breathing sounds with high quality during both wakefulness (short-period recording) and during sleep (long hours recording) that uses two small microphones that are placed in contact with the skin over the trachea and lung, respectively. The hardware device will be utilized to optimize the AWakeOSA algorithm and work towards achieving a reliable home-care device for screening under unsupervised conditions. The proposed technology will enable a reliable and quick diagnosis of OSA that can be either used in a clinician's office during wakefulness and/or used at home by people to monitor their own OSA. The outcomes of this study will benefit the health care system and society significantly as it will: 1) reduce the financial burden of OSA on the healthcare system by reducing the need for PSG and unnecessary preoperative resources; and 2) provide a quick and reliable personal OSA home-care monitoring system for better OSA treatment management.

Conditions

Eligibility

Locations (1)

Outcomes