Highlights

In brief

Accelerometer and heart rate data from wearables can reveal sleep patterns with implications for health and disease.

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Going deep into the science of sleep

30 Aug 2019

A*STAR researchers have developed a deep learning method to more accurately measure sleep duration and quality.

If you wake up tired, it’s easy enough to deduce that you’ve had a bad night’s sleep. But measuring one’s sleep patterns in more detail usually requires polysomnography, which involves trying to fall asleep in a sleep clinic or hospital bed while wired up to sensors for heart rate, blood oxygen, body movement and other vital signs. Recording and understanding sleep patterns are vital for physical and mental health, especially for those suffering from sleep disorders, but polysomnography is cumbersome and expensive.

Now, researchers at A*STAR’s Institute for Infocomm Research (I2R), in collaboration with McLaren Applied Technologies, have combined wearable sensors and machine learning techniques to devise an alternative to polysomnography. Zhenghua Chen, lead researcher on the team, notes that movement sensors have become small and cheap enough that people routinely wear them throughout the day. As such, it should also be possible to use these sensors to record sleeping and waking through the night, then analyze the data for health insights using machine learning techniques.

“However, conventional machine learning approaches for sleep-wake detection require features of sleep-wake cycles to be manually defined by an expert, and some implicit features may be missed out,” Chen explained. To address this problem, the team developed a deep learning framework that automatically distinguishes sleep and wake phases based on accelerometer readings and heart rate variability (HRV) measurements derived from wearable devices.

The researchers first had to account for the high sampling rate of accelerometer readings, which creates many blocks of sequential time series data. Instead of analyzing all blocks at once, they applied a ‘divide and conquer’ strategy, whereby the dataset is broken into smaller segments and analyzed for local features representative of sleep and wakefulness. They then designed a ‘fusion framework’ to merge accelerometer data with HRV measurements to automatically define and detect sleep-wake cycles.

“Representative features of sleep-wake cycles can thus be automatically learned without human intervention. Compared with state-of-the-art methods, our approach improves the accuracy of sleep-wake detection by three to 19 percent. Hence, better detection performance can be achieved with our proposed method,” said Chen.

Going forward, Chen’s team plans to further develop their deep learning technique to identify more sleep stages, such as light sleep, deep sleep and rapid eye movement (REM) sleep. A finer understanding of sleep-wake cycles could help identify and diagnose many sleep disorders, said Chen.

The A*STAR-affiliated researchers contributing to this research are from the Institute of Infocomm Research (I2R).

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References

Chen, Z., Wu, M., Wu, J., Ding, J., Zeng, Z., et al. Deep Learning Approach for Sleep-Wake Detection from HRV and Accelerometer Data. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2019 | article

About the Researcher

Zhenghua Chen earned his BEng degree in Mechatronics Engineering from the University of Electronic Science and Technology of China in Chengdu in 2011 and his PhD in Electrical and Electronic Engineering from Nanyang Technological University, Singapore, in 2017. He is now a Scientist and Lab Head at the Institute for Infocomm Research, and an Early Career Investigator at the Centre for Frontier AI Research (CFAR). He has received numerous awards, including first place at the CVPR 2021 UG2+ Challenge, the A*STAR Career Development Award, first runner-up at the IEEE VCIP 2020 Grand Challenge, and best paper at IEEE ICIEA and IEEE SmartCity, both in 2022. He serves as Associate Editor for several IEEE and Springer journals. Chen is the Chair of the IEEE Sensors Council Singapore Chapter and an IEEE Senior Member. His research focuses on data-efficient and model-efficient learning, with applications in smart cities and smart manufacturing.

This article was made for A*STAR Research by Wildtype Media Group