A Dual-Scale Convolutional Neural Network for Sleep Apnea Detection with Time-Delayed SpO2 Signals

Sleep apnea (SA) is a common breathing disease, with clinical manifestations of sleep snoring at night with apnea and daytime sleepiness. It could lead to ischemic heart disease, stroke, or even sudden death. SpO(2) signal is highly related to SA, and many automatic SA detection methods have been proposed. However, extant work focuses on small datasets with relatively few subjects (less than 100) and is unaware of SA syndromes occurring about 5 seconds prior to the SpO(2) change. This study proposes an automatic SA detector called DSCNN using a single-lead SpO(2) signal with a dual-scale convolutional neural network. To solve the time-delayed problem of SpO(2) changes, we enlarge the target SpO(2) segment information by combining its subsequent segment information. To utilize neighbouring segments information and further facilitate the SA detection performance, a dual-scale neural network with the fusing information of the prolonged target segment and its two surrounding segments is proposed. Three datasets from multiple centres are employed to verify the generic performance of DSCNN. Here, we must point out that we use two datasets as external datasets, and one of them is collected from the First Affiliated Hospital of Sun Yat-sen University with a large sample size (450 subjects). Extensive experiment results show that DSCNN can achieve promising results which are superior to the existing state-of-the-art methods.