A Deep Learning Approach for Sleep-Wake Detection from HRV and Accelerometer Data

A Deep Learning Approach for Sleep-Wake Detection from HRV and Accelerometer Data
Title:
A Deep Learning Approach for Sleep-Wake Detection from HRV and Accelerometer Data
Other Titles:
2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
DOI:
10.1109/BHI.2019.8834502
Publication Date:
19 May 2019
Citation:
Z. Chen et al., "A Deep Learning Approach for Sleep-Wake Detection from HRV and Accelerometer Data," 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Chicago, IL, USA, 2019, pp. 1-4. doi: 10.1109/BHI.2019.8834502
Abstract:
Sleep-wake classification is important for measuring the sleep quality. In this paper, we propose a novel deep learning framework for sleep-wake detection by using acceleration and heart rate variability (HRV) data. Firstly, considering the high sampling rate of acceleration data with temporal dependency, we propose a local feature based long short-term memory (LF-LSTM) approach to learn high-level features. Meanwhile, we manually extract representative features from HRV data, as HRV data has a distinct format with acceleration data. Then, a unified framework is developed to combine the features learned by the LF-LSTM from acceleration data and the features extracted from HRV data for sleep-wake detection. We use real data to evaluate the performance of the proposed framework and compare it with some benchmark approaches. The results show that the proposed approach achieves a superior performance over all the benchmark approaches for sleep-wake detection.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2019 IEEE.
ISSN:
2641-3604
2641-3590
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