SPTESleepNet: Automatic Sleep Staging Model Based On Strip Patch Embeddings And Transformer Encoder

Abstract

Although the research on automatic sleep staging has made great progress, there is still a certain distance from its clinical application. For a machine scoring system, in order to work in an interactive and collaborative manner with practitioners, two barriers need to be addressed, which are high accuracy and high efficiency. In this paper, we proposed a sleep staging model, named SPTESleepNet, as a stepping stone towards addressing the two above-mentioned obstacles. SPTESleepNet relies on the concept of Transformer, gives up the traditional convolution and recurrent methods, building a model based on Transformer framework with strip patch embedding. Overall the experimental results of SPTESleepNet on the databases showed that our model outperforms the previous sleep staging methods and improves the stateof-art results on these databases. On the larger database, Sleep-EDF-78, SPTESleep achieved an overall accuracy, macro F1-score, and Cohen’s kappa of 90.3%, 86.8% and 87.0%. Furthermore, on the smaller database, Sleep-EDF-20, SPTESleep surmounted the weakness that the Transformer models depend on large sample data to train, achieving an overall accuracy, macro F1-score, and Cohen’s kappa of 96.6%, 95.5%, 95.0%.