Learning From Single-Expert Annotated Labels for Automatic Sleep Staging

Abstract

Existing automatic sleep staging algorithms rely on accurately labeled data. However, due to the subjectivity of sleep experts, accurate labels must be obtained through joint labeling by multiple experts, which results in high time and labor costs. In this work, we treat labels mislabeled by a single expert as noisy labels and first propose SE-ASS, an automatic sleep staging learning framework based on single-expert annotated data. Since multiple models tend to produce inconsistent predictions for instances with incorrect labels during training, we use two networks with the same structure but different initializations and regularize them with a prediction consistency loss to prevent overfitting to noisy labels. Furthermore, we use a contrastive loss between models to enhance the exploration of feature representations without relying on potentially noisy labels. Our results on two publicly available datasets show that SE-ASS can effectively improve the performance of automatic sleep staging models trained on single-expert annotated datasets.