Cross-Individual Obstructive Obstructive Apnea Detection in Snoring Signals Using Hybrid Deep Neural Networks

Sleep apnea syndrome (SAS) is a common sleep problem, among which obstructive sleep apnea (OSA) is the most common. It is estimated that 936 million adults aged 30-69 years suffer from mild to severe obstructive sleep apnea that can result in poor sleep quality and even endanger their lives. In our study, 2051 OSA snoring fragments and 2271 normal snoring fragments were collected, and then the two were classified by the hybrid neural network. The most important innovation of this paper is the cross-individual snoring classification, which is different from the previous work, making the model more generalized. The experimental dataset was from 24 patients, the snores of 20 patients were used for the training model, and the snores of 4 people were used for the test. Finally, the accuracy of classification on the test set was 73.75%, and a portable snore classification platform is realized by using an embedded platform and edge computing.