Real-time acoustic apnea event detector by training a deep learning model with home noise added data

May 29, 2023
Sleep 2023

Abstract

Introduction:

For diagnosis and management of Obstructive Sleep Apnea (OSA), long-term multi-night monitoring is cru- cial. Convenient detection of OSA at home is required for this purpose. Using sound recorded by smartphone can provide a convenient way to detect OSA. In this study, we present a sound- based OSA detection deep learning model that can detect OSA in real-time even in a home environment where various noises exist. The model is trained with home noise simulated sound to be robust for detecting home noises.

Methods:

Two types of data were used for training and testing. The first type was sleep breathing sound data collected at the hospital while patients underwent a PSG. It included 1,154 and 297 nights recorded by a PSG microphone and a smartphone, respectively. We split them into 150 nights of smartphone data for testing and the rest for training. The second type was home noise data, which included 22,500 noises that might occur in a residen- tial environment. The proposed acoustic apnea event detector inputs Mel spectrograms of sleep breathing sounds and outputs OSA event classes for each epoch (APNEA, HYPOPNEA, or NO-EVENT). 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-hy- popnea index (AHI).

Results:

Our model achieved 86 % epoch-by-epoch agreement (0.75 in macro F1) 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.

Conclusion:

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

Authors

Linh Le
Daewoo Kim
Eunsung Cho
Hyeryung Jang
Roben Delos Reyes
Hyunggug Kim
Dongheon Lee
In-Young Yoon
Joonki Hong
Jeong-Whun Kim

Acknowledgments