LAUSR

Sleep apnea syndrome (SAS) is a common chronic respiratory disorder, which seriously harms human health. In order to realize the large-scale promotion of SAS detection, the SAS detection method based on wearable devices has attracted the attention of some researchers. However, in view of the poor signals collected by wearable devices and the less variation between signals of different classes (normal/SAS), the detection methods in past studies are often unsatisfactory. In this study, a wearable SAS detection method based on 1-D multitask multiattention residual shrinkage convolution neural network (1D-MMResSNet) model and cost-sensitive (CS) classifier was proposed. First, the photoplethysmography (PPG) sleep data of 92 subjects were collected by using wearable smart Bracelet devices. Second, a backbone network composed of the residual multiattention mechanism convolution block and residual shrinkage convolution block was proposed to realize feature selection and feature extraction for pulse rate variability signals. At the same time, the single supervised learning task of the network was improved, and a multitask learning method was proposed to enhance the ability of the network to mine subtle differences between data, thereby effectively learning the discriminative network features. Finally, the AdaCost cost-sensitive algorithm was introduced to alleviate the class imbalance problem in data samples. In segment detection, the accuracy, sensitivity, and specificity were 81.82%, 70.27%, and 85.81%, respectively; In individual detection, the accuracy, sensitivity, and specificity were 95.65%, 88.89%, and 97.30%, respectively. Experimental results show that the proposed model has excellent detection performance and is expected to be embedded in wearable devices.