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

May 29, 2023
Sleep 2023



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.


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).


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.


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.


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