期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 103, 期 -, 页码 71-81出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2018.10.010
关键词
Sleep stage; Classification; Feature learning; Recurrent neural networks; Wearable device
类别
资金
- Microsoft Research under the eHealth program
- National Natural Science Foundation of China [81771910]
- National Science and Technology Major Project of the Ministry of Science and Technology in China [2017YFC0110903]
- Beijing Natural Science Foundation in China [4152033]
- Technology and Innovation Commission of Shenzhen in China [shenfagai 2016-627]
- Beijing Young Talent Project in China
- Fundamental Research Funds for the Central Universities of China from the State Key Laboratory of Software Development Environment in Beihang University in China [SKLSDE-2017ZX-08]
- 111 Project in China [B13003]
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-1 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.
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