An effective multi-model fusion method for EEG-based sleep stage classification

Abstract Stage 1 (S1) and REM sleep are the two key stages in EEG-based sleep stage classification, which are of great significance to the study of neurocognitive ability and sleep diseases. Recently, various methods have been widely studied, and achieved good classification performance, however, most existing studies have a common problem of the low detection rate of S1 and REM sleep. In this paper, we focus on improving the detection performance of S1 and REM sleep and present an effective multi-model fusion method by using hybrid-channel EEG signals, which consists of two parts: the detection of merged stage of S1 and REM sleep and the classification between these two stages. First, we detect S1 and REM sleep by distinguishing the merged stage from other sleep stages using C-SVM model and single-channel EEG signals. To overcome the influence caused by class imbalance, a one-class OC-SVM model of the merged stage is established to correct S1 and REM sleep from the misclassified negative samples. Then, through analyzing the EEG characteristic between S1 and REM sleep and extracting the classification features of multiple sub-bands, we classify S1 and REM sleep using two-channel EEG signals. Finally, the proposed method is tested and analyzed for commonly used dataset of Sleep-EDFX. The results show that this method can effectively detect S1 and REM sleep and promote the application of sleep quality evaluation, fatigue detection, sleep disease diagnosis.