Sleep Quality Prediction From Wearable Data Using Deep Learning
Background
The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science.
Objective
The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality.
Methods
Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM).
Results
Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional logistic regression (0.6463).
Conclusions
Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.
Correction of: Sleep Quality Prediction From Wearable Data Using Deep Learning. https://dx.doi.org/10.2196/mhealth.6953, published online 25 November 2016.
Other Information
Published in: JMIR mHealth and uHealth
License: https://creativecommons.org/licenses/by/2.0/
See article on publisher's website: https://dx.doi.org/10.2196/mhealth.6562
History
Language
- English
Publisher
JMIR PublicationsPublication Year
- 2016
License statement
This Item is licensed under the Creative Commons Attribution 2.0 Generic License.Institution affiliated with
- Hamad Bin Khalifa University
- Qatar Computing Research Institute - HBKU
- Weill Cornell Medical College in Qatar (2001-2015)
- Weill Cornell Medicine - Qatar