Xin Zhang1, Weixuan Kou2, Eric I-Chao Chang3, He Gao4, Yubo Fan5, Yan Xu6. 1. School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China. Electronic address: xinzhang0376@gmail.com. 2. School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China. Electronic address: weixuankou@outlook.com. 3. Microsoft Research Asia, Beijing, 100080, China. Electronic address: echang@microsoft.com. 4. Clinical Sleep Medicine Center, The General Hospital of the Air Force, Beijing, 100142, China. Electronic address: bjgaohe@sohu.com. 5. School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China; Research Institute of Beihang University in Shenzhen and the Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and the State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100191, China. Electronic address: yubofan@buaa.edu.cn. 6. School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China; Research Institute of Beihang University in Shenzhen and the Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and the State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100191, China; Microsoft Research Asia, Beijing, 100080, China. Electronic address: xuyan04@gmail.com.
Abstract
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 F1 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.
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 F1 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.
Authors: Haoqi Sun; Wolfgang Ganglberger; Ezhil Panneerselvam; Michael J Leone; Syed A Quadri; Balaji Goparaju; Ryan A Tesh; Oluwaseun Akeju; Robert J Thomas; M Brandon Westover Journal: Sleep Date: 2020-07-13 Impact factor: 5.849
Authors: Pin-Wei Chen; Megan K O'Brien; Adam P Horin; Lori L McGee Koch; Jong Yoon Lee; Shuai Xu; Phyllis C Zee; Vineet M Arora; Arun Jayaraman Journal: Sensors (Basel) Date: 2022-08-18 Impact factor: 3.847