An Attention Based CNN-LSTM Approach for Sleep-Wake Detection With Heterogeneous Sensors

• Published in: IEEE journal of biomedical and health informatics Vol. 25; no. 9; pp. 3270 - 3277
• Main Authors: Chen, Zhenghua, Wu, Min, Cui, Wei, Liu, Chengyu, Li, Xiaoli
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Artificial neural networks; attention; Brain modeling; CNN-LSTM; Deep learning; Feature extraction; Heart rate; Heart rate variability; HRV; Learning; Long short-term memory; Neural networks; Sensors; Sleep; Sleep and wakefulness; sleep-wake detection

In this article, we propose an attention based convolutional neural network long short-term memory (CNN-LSTM) approach for sleep-wake detection with heterogeneous sensor data, i.e., acceleration and heart rate variability (HRV). Since the three-dimensional acceleration data was sampled with a high f...