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The objective of this paper is to learn semantic representations for sleepstage classification from raw physiological time series. Although supervisedmethods have gained remarkable performance, they are limited in clinicalsituations due to the requirement of fully labeled data. Self-supervisedlearning (SSL) based on contrasting semantically similar (positive) anddissimilar (negative) pairs of samples have achieved promising success.However, existing SSL methods suffer the problem that many semantically similarpositives are still uncovered and even treated as negatives. In this paper, wepropose a novel SSL approach named SleepPriorCL to alleviate the above problem.Advances of our approach over existing SSL methods are two-fold: 1) byincorporating prior domain knowledge into the training regime of SSL, moresemantically similar positives are discovered without accessing ground-truthlabels; 2) via investigating the influence of the temperature in contrastiveloss, an adaptive temperature mechanism for each sample according to priordomain knowledge is further proposed, leading to better performance. Extensiveexperiments demonstrate that our method achieves state-of-the-art performanceand consistently outperforms baselines.

SleepPriorCL: Contrastive Representation Learning with Prior Knowledge-based Positive Mining and Adaptive Temperature for Sleep Staging