Multi-View Consistency Contrastive Learning With Hard Positives for Sleep Signals

Contrastive learning has successfully addressed the scarcity of large-scale labeled datasets, especially in the physiological time series field. Existing methods construct easy positive pairs as substitutes for ground truth based on temporal dynamics or instance consistency. Despite the potential of hard positive samples to provide richer gradient information and facilitate the acquisition of more discriminative representations, they are frequently overlooked in sampling strategies, thus constraining the classification capacity of models. In this letter, we focus on multi-view physiological signals and propose a novel hard positive sampling strategy based on the view consistency. Multi-view signals are recorded from sensors attached to different organs of human body. Additionally, we propose a Multi-View Consistency Contrastive (MVCC) learning framework to jointly extract intra-view temporal dynamics and inter-view consistency features. Experiments have been carried out on two public datasets and our method demonstrates state-of-the-art performance, achieving 83.25% and 73.37% accuracy on SleepEDF and ISRUC, respectively.