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This paper proposes a practical approach for automatic sleep stageclassification based on a multi-level feature learning framework and RecurrentNeural Network (RNN) classifier using heart rate and wrist actigraphy derivedfrom a wearable device. The feature learning framework is designed to extractlow- and mid-level features. Low-level features capture temporal and frequencydomain properties and mid-level features learn compositions and structuralinformation of signals. Since sleep staging is a sequential problem withlong-term dependencies, we take advantage of RNNs with Bidirectional LongShort-Term Memory (BLSTM) architectures for sequence data learning. To simulatethe actual situation of daily sleep, experiments are conducted with a restinggroup in which sleep is recorded in resting state, and a comprehensive group inwhich both resting sleep and non-resting sleep are included.We evaluate thealgorithm based on an eight-fold cross validation to classify five sleep stages(W, N1, N2, N3, and REM). The proposed algorithm achieves weighted precision,recall and F1 score of 58.0%, 60.3%, and 58.2% in the resting group and 58.5%,61.1%, and 58.5% in the comprehensive group, respectively. Various comparisonexperiments demonstrate the effectiveness of feature learning and BLSTM. Wefurther explore the influence of depth and width of RNNs on performance. Ourmethod is specially proposed for wearable devices and is expected to beapplicable for long-term sleep monitoring at home. Without using too much priordomain knowledge, our method has the potential to generalize sleep disorderdetection.

Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device