MtCLSS: Multi-Task Contrastive Learning for Semi-Supervised Pediatric Sleep Staging

The continuing increase in the incidence and recognition of children's sleep disorders has heightened the demand for automatic pediatric sleep staging. Supervised sleep stage recognition algorithms, however, are often faced with challenges such as limited availability of pediatric sleep physicians and data heterogeneity. Drawing upon two quickly advancing fields, i.e., semi-supervised learning and self-supervised contrastive learning, we propose a multi-task contrastive learning strategy for semi-supervised pediatric sleep stage recognition, abbreviated as MtCLSS. Specifically, signal-adapted transformations are applied to electroencephalogram (EEG) recordings of the full night polysomnogram, which facilitates the network to improve its representation ability through identifying the transformations. We also introduce an extension of contrastive loss function, thus adapting contrastive learning to the semi-supervised setting. In this way, the proposed framework learns not only task-specific features from a small amount of supervised data, but also extracts general features from signal transformations, improving the model robustness. MtCLSS is evaluated on a real-world pediatric sleep dataset with promising performance (0.80 accuracy, 0.78 F1-score and 0.74 kappa). We also examine its generality on a well-known public dataset. The experimental results demonstrate the effectiveness of the MtCLSS framework for EEG based automatic pediatric sleep staging in very limited labeled data scenarios.

Automated summary

The demand for automatic pediatric sleep staging has increased due to the rising incidence and recognition of children's sleep disorders. The existing supervised sleep stage recognition algorithms face challenges such as limited availability of pediatric sleep physicians and data heterogeneity. To address this, we propose a multi-task contrastive learning strategy that combines semi-supervised learning and self-supervised contrastive learning, named MtCLSS. By applying signal-adapted transformations and an extended contrastive loss function, MtCLSS learns task-specific and general features from limited labeled data, improving the robustness of the model for EEG based automatic pediatric sleep staging in limited data scenarios.