Design of embedded real-time system for snoring and OSA detection based on machine learning

Obstructive sleep apnea (OSA) is a common sleep disorder. As a gold standard to detect OSA, the polysomnography (PSG) is widely used. However, the acquisition of nighttime PSG signals is very inconvenient. Compared to PSG, snoring-based OSA detection is low-cost, convenient and non-invasive. In this work, the apnea situation is studied based on the nighttime snoring signals to detect OSA. Firstly, a nighttime audio database regarding snoring sounds is built by the multi-threshold endpoint detection from original night recordings. Next, feature extraction is carried out on the segregated sound signals to obtain the characteristic information of snoring. Then, feature categories are predicted based on the machine learning models. The proposed algorithm is transplanted to the embedded device to build a real-time system which can detect snoring and OSA events. The study experiments with a variety of detection schemes and finally trains a multi-classification temporal convolutional network (TCN) to classify night audio as non-snoring, snoring or OSA-related snoring. The experiment results have achieved good performance that the detection accuracy of OSA-related snoring is up to 96.7 %. In order to further promote this method, the trained TCN model is transplanted to an embedded device to detect snoring and OSA events using as a real-time system. The experimental results fully show that the proposed detection scheme can achieve high accuracy for the detection of snoring and OSA events, which demonstrates that it can be used a new tool to conveniently help potential OSA patients understand their sleep health for home health care.