Rethinking Sleep Staging: Tapping the Potential of Novel Label Encoding Beyond Network Designs

Sleep staging is an essential step in sleep quality assessment and sleep disorder diagnosis. However, most neural network approaches to automatic sleep staging have focused on improving network architectures and data augmentation techniques, the latent potential of sleep staging labels has not been fully explored. Existing approaches often use the one-hot encoding method, which encodes each sleep stage as an independent and parallel label, fail to capture the prior information and relationships among sleep stages, thereby limiting their potential. In this study, we propose a novel label encoding method called Binary Carry-Forward Encoding (BCFE) for sleep staging classification. Unlike traditional approaches with one-hot encoding, we first decompose the 5-class sleep staging task into four binary classification tasks aligned with the logical progression of sleep stages. By introducing 4-dimensional binary vectors, BCFE embeds potentially prior information into the label structure. Predicted labels are generated through Human-Like Progression (HLP) inference, and training is optimized with a Masked Binary CrossEntropy (MBCE) loss. These methods enables the network to better utilize nuanced label characteristics, enhancing classification performance. The evaluation was conducted across combinations of three models (DeepSleepNet, AttnSleep and SleePyCo) and two datasets (ISRUC-S3 and Sleep-EDF). The results showed that our proposed method demonstrated significant improvements compared to the traditional One-Hot + Cross-Entropy method, particularly for the DeepSleepNet architecture on the ISRUC-S3 dataset. Notably, it achieved nearly 7% increase in performance (Accuracy: 73.7% → 80.3%; Macro-F1: 71.3% → 78.8%; kappa: 66.7% → 74.6%).