LAUSR

Sleep spindles are closely associated with cognitive functions and neurological disorders; thus, spindle detection has been an important topic in sleep medicine. Recently, machine learning approaches have shown the potential in automatic sleep spindle detection by learning optimized features in a data-driven way, while they highly rely on labeled data, and the performance can be degraded when labels are inaccurate. However, accurate annotation of the spindle is usually difficult to obtain and high intraexpert and interexpert variance exist. In this work, we propose a convolutional neural network (CNN) with a label refinement component to learn an effective spindle detector with imperfect labels. Our approach consists of two stages: 1) a feature learning stage and 2) a label refinement stage. In the feature learning stage, a CNN-based multiple instance learning framework (CNN-MIL) is built for spindle feature learning. By assuming only parts of each labeled spindle segment contain true spindle patterns, the CNN-MIL model can learn most-likely spindle-related features from ambiguous labels. In the label refinement stage, we adjust the spindle labels by merging the original labels and labels predicted by CNN-MIL, and the modified labels are then used in the next round CNN-MIL feature learning. The two stages perform alternately for detector optimization. Extensive experiments demonstrated that our approach achieved the state-of-the-art performance.