Gaussian transformation enhanced semi-supervised learning for sleep stage classification

Sleep disorders are significant health concerns affecting a large population. Related clinical studies face the deficiency in sleep data and challenges in data analysis, which requires enormous human expertise and labor. Moreover, in current clinical practice, sleep data acquisition processes usually cover only one night's sleep history, which is too short to recognize long-term sleep patterns. To address these challenges, we propose a semi-supervised learning (cluster-then-label) approach for sleep stage classification, integrating clustering algorithms into the supervised learning pipeline. We test the effectiveness of the proposed semi-supervised learning approach on two architectures: an advanced architecture using deep learning for classification and k-means for clustering, and a relatively naive Gaussian-based architecture. Also, we introduce two novel Gaussian transformations to improve the robustness and accuracy of the Gaussian-based architecture: assembled-fixed transformation and neural network based transformation. We reveal the effectiveness of the proposed algorithm via experiments on whole-night electroencephalogram (EEG) data. The experiments demonstrate that the proposed learning strategy improves the accuracy and F1 score over the state-of-the-art baseline on out-of-distribution human subjects. The experiments also confirm that the proposed Gaussian transformations can significantly gain normality to EEG band-power features and in turn facilitate the semi-supervised learning process. This cluster-then-label learning approach, combined with novel Gaussian transformations, can significantly improve the accuracy and efficiency of sleep stage classification, enabling more effective diagnosis of sleep disorders.