Abstract: Sleep takes up nearly one-third of a person’s day and is closely related to human health. Since the durations and transitions over different sleep stages during sleep directly affect a person's sleep quality, identifying sleep stages has become the most basic and important task in sleep monitoring. However, sleep disorders occurring in sleep can lead to complex sleep structures, thereby increasing the difficulty of classifying sleep stages. Most of the existing contactless solutions for sleep stage classification lack a sufficient understanding of the complexity in sleep structure, ignoring the relationship between sleep stage and sleep disorder. Therefore, these solutions fail to achieve great performance in patients with sleep disorders. In this paper, we propose a sleep monitoring system that focuses on predicting sleep stages from ultra-wideband (UWB) signals. We design a sequence prediction model that combines an attention-based sequence encoder and a contrastive learning module to extract the temporal progression of sleep and improve the generalizability of the encoder. Particularly, the key to our approach is a multi-task fine-tuning strategy that incorporates sleep disorder information into sleep staging to reduce the interference of sleep disorders with sleep stage prediction. We conduct extensive experiments on 110 subjects, including healthy individuals and patients with different severities of sleep disorders. The experimental results demonstrate that the performance of our model is superior to the baseline methods.