A Novel Ensemble Deep Learning Approach for Sleep-Wake Detection Using Heart Rate Variability and Acceleration

Sleep-wake detection is of great importance for the measurement of sleep quality. In this article, a novel ensemble deep learning framework is proposed to detect sleep-wake states based on heart rate variability (HRV) and acceleration. We firstly design a local feature based long short-term memory (LF-LSTM) network to encode temporal dependency and learn features from acceleration data with high sampling frequency. In the meantime, some handcrafted features are extracted from HRV which has a special data format. After that, we develop a unified framework to integrate these two types of features, i.e., the features extracted from HRV and the features learned by LF-LSTM from acceleration, to form a complete feature set. Finally, an efficient ensemble learning scheme is proposed to further boost the performance of sleep-wake classification. A real dataset has been collected to verify the effectiveness of the proposed approach. We also compare with some well-known benchmark approaches for sleep-wake detection. The results demonstrate that the proposed ensemble deep learning method outperforms all the benchmark approaches.

Automated summary

Sleep-wake detection is crucial for assessing sleep quality. A novel ensemble deep learning framework combining heart rate variability and acceleration data has been proposed in this article. By integrating LF-LSTM network and handcrafted HRV features, the performance of sleep-wake classification is significantly boosted.