Sleep stage classification based on multi-level feature learning and recurrent neural networks via wearable device.

BACKGROUND

Automatic sleep stage classification is essential for long-term sleep monitoring. Wearable devices show more advantages than polysomnography for home use. In this paper, we propose a novel method for sleep staging using heart rate and wrist actigraphy derived from a wearable device.

METHODS

The proposed method consists of two phases: multi-level feature learning and recurrent neural networks-based (RNNs) classification. The feature learning phase is designed to extract low- and mid-level features. Low-level features are extracted from raw signals, capturing temporal and frequency domain properties. Mid-level features are explored based on low-level ones to learn compositions and structural information of signals. Sleep staging is a sequential problem with long-term dependencies. RNNs with bidirectional long short-term memory architectures are employed to learn temporally sequential patterns.

RESULTS

To better simulate the use of wearable devices in the daily scene, experiments were conducted with a resting group in which sleep was recorded in the resting state, and a comprehensive group in which both resting sleep and non-resting sleep were included. The proposed algorithm classified five sleep stages (wake, non-rapid eye movement 1-3, and rapid eye movement) and achieved weighted precision, recall, and F score of 66.6%, 67.7%, and 64.0% in the resting group and 64.5%, 65.0%, and 60.5% in the comprehensive group using leave-one-out cross-validation. Various comparison experiments demonstrated the effectiveness of the algorithm.

CONCLUSIONS

Our method is efficient and effective in scoring sleep stages. It is suitable to be applied to wearable devices for monitoring sleep at home.