SEMI-SUPERVISED SPARSE REPRESENTATION CLASSIFICATION FOR SLEEP EEG RECOGNITION WITH IMBALANCED SAMPLE SETS

Abstract

Sleep staging with supervised learning requires a large amount of labeled data that are time-consuming and expensive to collect. Semi-supervised learning is widely used to improve classification performance by combining a small amount of labeled data with a large amount of unlabeled data. The accuracy of pseudo-labels in semi-supervised learning may influence the performance of classifier. Based on semi-supervised sparse representation classification, this study proposed an improved sparse concentration index to estimate the confidence of pseudo-labels data for sleep EEG recognition considering both interclass differences and intraclass concentration. In view of class imbalance in sleep EEG data, the synthetic minority oversampling technique was also improved to remove mixed samples at the boundary between minority and majority classes. The results showed that the proposed method achieved better classification performance, in which the classification accuracy after class balancing was obviously higher than that before class balancing. The findings of this study will be beneficial for application in sleep monitoring devices and sleep-related diseases.