Paper Info
Title
Sleep Stage Classification Using Bidirectional LSTM in Wearable Multi-sensor Systems
Abstract
Understanding the sleep quality and architecture is essential to human being's health, which is usually represented using multiple sleep stages. A standard sleep stage determination requires Electroencephalography (EEG) signals during the expensive and labor-intensive Polysomnography (PSG) test. To overcome this inconvenience, cardiorespiratory signals are proposed for the same purpose because of the easy and comfortable acquisition by simplified devices. In this paper, we leverage our low-cost wearable multi-sensor system to acquire the cardiorespiratory signals from subjects. Three novel features are designed during the feature extraction. We then apply a Bidirectional Recurrent Neural Network architecture with Long Short-term Memory (BLSTM) to predict the four-class sleep stages. Our prediction accuracy is 80.25% on a large public dataset (417 subjects), and 80.75% on our 32 enrolled subjects, respectively. Our results outperform the previous works which either used small data sets and had the potential over-fitting issues, or used the conventional machine learning methods on large data sets.
Year
DOI
Venue
2019
10.1109/INFCOMW.2019.8845115
IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Keywords
Field
DocType
—Sleep stage,Wearable sensors,Healthcare,Deep learning
Data set,Small data,Wearable computer,Computer science,Recurrent neural network,Feature extraction,Artificial intelligence,Machine learning,Sleep Stages,Electroencephalography,Polysomnography
Conference
ISSN
ISBN
Citations 
2159-4228
978-1-7281-1879-6
0
PageRank 
References 
Authors
0.34
9
9
Name
Order
Citations
PageRank
Yuezhou Zhang100.34
Zhicheng Yang221.74
Ke Lan352.25
Xiaoli Liu462.37
Zhengbo Zhang562.71
Peiyao Li672.40
Desen Cao783.09
Jiewen Zheng882.42
Jianli Pan947133.61