Advancing Wakefulness Screening of Sleep Apnea Disorder through Breathing Sound Analysis and Hardware Optimization

Project Summary/Abstract
Sleep apnea affects over one billion people worldwide, with obstructive sleep apnea (OSA) accounting for more
than 85% of cases. OSA is characterized by repeated episodes of airway obstruction during sleep, leading to
fragmented sleep and reduced oxygen levels. OSA is linked to several health complications, including
cardiovascular disease, diabetes, and cognitive impairment. Despite its prevalence, OSA remains significantly
underdiagnosed, especially in underserved populations, due to limited access to gold-standard diagnostics like
overnight polysomnography (PSG). Early diagnosis is essential to prevent long-term health issues and alleviate
the associated healthcare burden. Additionally, a quick and objective OSA screening could prevent unnecessary
surgical precautions due to the low specificity (~40%) of widely used questionnaires, such as STOP-BANG. This
project focuses on enhancing a wakefulness-based sleep apnea screening tool that utilizes breathing sound
analysis to assess sleep apnea risk. The tool offers a non-invasive, objective, and accessible screening method,
especially for populations with limited access to traditional sleep studies. However, the tool’s screening accuracy,
affordability, and usability must be improved for large-scale implementation. We propose three specific aims to
address these challenges. Aim 1 seeks to refine the tool’s algorithm by reducing the influence of anthropometric
factors on sound feature classification and incorporating intra-subject variance to enhance reliability.
Deterministic and non-deterministic machine learning algorithms will be integrated to improve classification
outcomes. Aim 2 will apply machine learning, advanced signal processing, and statistical analysis techniques to
identify acoustic features that correlate with and predict the apnea-hypopnea index (AHI) and other sleep metrics,
such as oxygen desaturation index and apnea index. Aim 3 focuses on the development of custom hardware
and a microphone chamber for improved breathing sound recording and processing, aiming to create a more
portable, cost-effective, and user-friendly device for broad adoption. Our long-term goal is to provide an
accessible, wakefulness-based screening tool for early detection of sleep apnea and assessment of sleep quality
metrics, particularly in underserved populations and individuals undergoing surgeries requiring full anesthesia.
This tool has the potential to reduce diagnostic time and costs, improve early intervention rates, and decrease
the risk of untreated sleep apnea leading to severe health complications. The proposed R03 project will generate
key insights and models for future clinical trials and broader implementation. By improving the accuracy and
usability of this screening tool, the research aims to address critical public health needs, especially in rural or
low-resource settings where access to conventional sleep diagnostics is limited. If successful, the tool could
significantly impact sleep apnea diagnosis and treatment, leading to better patient outcomes and a reduction in
healthcare costs associated with untreated sleep apnea.

Grant Number: 1R03EB037909-01
NIH Institute/Center: NIH
Principal Investigator: Elwali Ahmed