JMIR Publications

ABSTRACT

Background:

Multi-night monitoring can be helpful for diagnosis and management for obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real-time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full non-contact monitoring of OSA at home.

Objective:

The purpose of this study is to develop a predictive model that can detect OSA in real-time even in a home environment where various noises exist.

Methods:

This study included 1,154 PSG audio datasets, 297 smartphone audio datasets synced with PSG, and a home noise dataset containing 22,500 noises to train the model to predict breathing events such as apneas and hypopneas based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as APNEA, HYPOPNEA, or NO-EVENT, and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed by epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI).

Results:

The epoch-by-epoch respiratory event detection showed an accuracy of 86% and a macro F1 of 0.75 for 3-class event detection task. The model had an accuracy of 92% for NO-EVENT, 84% for APNEA, and 51% for HYPOPNEA. Most misclassifications were made for HYPOPNEA, with 15% and 34% of HYPOPNEA being wrongly predicted as APNEA and NO-EVENT, respectively. The sensitivity and specificity of OSA severity classification (AHI ≥ 15) were 0.85 and 0.84, respectively.

Conclusions:

Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multi-night monitoring and real-time diagnostic technologies in the home environment.