Paper Info
Title
An Effective Deep Learning Approach for Unobtrusive Sleep Stage Detection Using Microphone Sensor
Abstract
Sleep plays a vital role in good health and well-being throughout human life. A great deal of studies have been done to detect sleep stages. Most of the current sleep monitoring systems are invasive to users, e.g. requiring users to wear a device during sleep. In this paper, we use microphone to detect sleep stages including deep sleep, light sleep and rapid eye movement (REM), and propose a convolutional neural network using spectrogram as input. This paper's contribution mainly concentrates on the following two aspects: First, microphone is unobtrusive for sleep detection. Second, we build the mapping between acoustic signal and sleep stages with little manual intervention to extract features. Performance of the proposed method is validated on a realistic environmental dataset containing 52 nights of 5 participants. Experimental results show that the accuracy of sleep stages detection is superior to the representative off-the-shelf applications. Besides, we propose to utilize the attention maps to visualize acoustic data to better understand the relationship between sound and sleep stages. Experimental results show that the model has the ability to effectively reduce noises in classification by ignoring the high-frequency sounds and white noises.
Year
DOI
Venue
2017
10.1109/ICTAI.2017.00018
2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)
Keywords
Field
DocType
sleep stage detection,acoustic signal,deep learning,unobtrusive
Mel-frequency cepstrum,Pattern recognition,Computer science,Spectrogram,Convolutional neural network,Eye movement,Artificial intelligence,Deep learning,Slow-wave sleep,Microphone,Sleep Stages
Conference
ISSN
ISBN
Citations 
1082-3409
978-1-5386-3877-4
0
PageRank 
References 
Authors
0.34
11
5
Name
Order
Citations
PageRank
Yu-xin Zhang1389.44
Yiqiang Chen21446109.32
Lisha Hu31037.45
Xinlong Jiang47610.70
Jianfei Shen544.21