Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection - Jiang, Xinge; Ren, YongLian; Wu, Hua; Li, Yanxiu; Liu, Feifei (Frontiers in Neuroscience, 2023)
Monitoring sleep quality can provide scientific guidance to ensure global sleep quality, but the current method of monitoring sleep disorder is complex, time-consuming and uncomfortable. This paper aims to seek a comfortable and convenient method for identifying sleep apnea syndrome. In this paper, a one-dimensional convolutional neural network model was established. The model was trained by the photoplethysmographic (PPG) signals of 20 healthy people and 39 sleep apnea syndrome (SAS) patients to classify SAS, and the influence of noise on the model was tested through anti-interference experiments. The results showed that the accuracy of the model for SAS classification exceeds 90%, and it has some anti-interference ability. This paper provides a SAS recognition method based on PPG signals, which is helpful for portable wearable detection.