EEG Sleep Stage Classification via Domain Similarity Detection and Trajectories in Riemannian Space

Sleep stage classification is crucial for diagnosing Obstructive Sleep Apnea (OSA). OSA patients’ sleep electroencephalography (EEG) signals often exhibit frequent oscillations due to abnormal apnea. Additionally, EEG signals are weak and nonlinear; it is more suitable to analyze EEG signals in the nonlinear space. Hence, we proposed a novel cross-subject EEG-based Sleep Stage Classification (EEGSSC) method for OSA patients in Riemannian manifold space. Firstly, each sleep EEG instance was converted into a sequence of symmetric positive definite matrices by calculating the multichannel covariance. Next, a domain similarity detection technique is introduced to select similar patients in the manifold space. Centroid alignment is then applied to minimize differences in marginal probability distributions between patients by aligning the Riemannian means of their covariance matrices. To extract the comprehensive features of the sleep EEG signals on the manifold, we not only used a transported square-root vector field to capture dynamic features but also computed static features by the log-Euclidean Riemannian metric. A multi-layer perceptron classifier is then used for classification. The proposed method has been tested on ISRUC and Dreem datasets, and the results demonstrate that EEGSSC can serve as an effective tool for automated sleep stage classification in OSA patients.