# Interpretable Acoustic Features from Wakefulness Tracheal Breathing for OSA Severity Assessment

## TL;DR

This study presents a non-invasive method using breathing sounds and body measurements to assess the severity of sleep apnea, offering a more accessible alternative to traditional tests.

## Contribution

The work introduces a machine-learning framework using interpretable acoustic features from tracheal breathing sounds for OSA severity classification.

## Key findings

- The framework effectively discriminates among four OSA severity groups using tracheal breathing sounds and anthropometric variables.
- Combining acoustic features with body measurements improves classification performance and reliability across all severity classes.
- The approach shows potential for scalable and accessible OSA screening, enabling earlier detection.

## Abstract

Background: Obstructive Sleep Apnea (OSA) is one of the most prevalent sleep disorders associated with cardiovascular complications, cognitive impairments, and reduced quality of life. Early and accurate diagnosis is essential. The present gold standard, polysomnography, is expensive and resource-intensive. This work develops a non-invasive machine-learning-based framework to classify four OSA severity groups (non, mild, moderate, and severe) using tracheal breathing sounds (TBSs) and anthropometric variables. Methods: A total of 199 participants were recruited, and TBS were recorded whilst awake (wakefulness) using a suprasternal microphone. The workflow included the following steps: signal preprocessing (segmentation, filtering, and normalization), multi-domain feature extraction representing spectral, temporal, nonlinear, and morphological features, adaptive feature normalization, and a three-stage feature selection that combined univariate filtering, Shapley Additive Explanations (SHAP)-based ranking, and recursive feature elimination (RFE). The classification included training ensemble learning models via bootstrap aggregation and validating them using stratified k-fold cross-validation (CV), while preserving the OSA severity and anthropometric distributions. Results: The proposed framework performed well in discriminating among OSA severity groups. TBS features, combined with anthropometric ones, increased classification performance and reliability across all severity classes, providing proof for the efficacy of non-invasive audio biomarkers for OSA screening. Conclusions: TBS-based model’s features, coupled with anthropometric information, offer a promising alternative or supplement to PSG for OSA severity detection. The approach provides scalability and accessibility to extend screening and potentially enables earlier detection of OSA, compared to cases that might remain undiagnosed without screening.