An Automatic Sleep Staging Model Combining Feature Learning and Sequence Learning

Sleep stage classification is a technique for analyzing sleep quality. Manual sleep staging is time-consuming and laborious. In this paper, we propose an automatic sleep stage classification model combining feature learning and sequence learning, which extract features with convolutional neural network(CNN) and learn the sequence transition rule through multi-layer long short term memory(LSTM) architecture with attention mechanism. In addition, we also noticed that most of the misclassified samples locate in transition period. Therefore, multi-label classification scheme is introduced to provide more label information, so as to improve the classification performance of transition period. We evaluate on two public datasets (Sleep EDF Expanded and Physionet2018), where our framework reaches macro F1-score of 79.7 and 79.8, respectively. The proposed network achieves the state-of-the-art classification performance on Sleep EDF Expanded dataset and sets new benchmark on Physionet2018 dataset.