Paper Info
Title
Non-Invasive Classification Of Sleep Stages With A Hydraulic Bed Sensor Using Deep Learning
Abstract
The quality of sleep has a significant impact on health and life. This study adopts the structure of hierarchical classification to develop an automatic sleep stage classification system using ballistocardiogram (BCG) signals. A leave-one-subject-out cross validation (LOSO-CS) procedure is used for testing classification performance. Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Deep Neural Networks DNNs are complementary in their modeling capabilities; while CNNs have the advantage of reducing frequency variations, LSTMs are good at temporal modeling. A transfer learning (TL) technique is used to pre-train our CNN model on posture data and then fine-tune it on the sleep stage data. We used a ballistocardiography (BCG) bed sensor to collect both posture and sleep stage data to provide a non-invasive, in-home monitoring system that tracks changes in health of the subjects over time. Polysomnography (PSG) data from a sleep lab was used as the ground truth for sleep stages, with the emphasis on three sleep stages, specifically, awake, rapid eye movement (REM) and non-REM sleep (NREM). Our results show an accuracy of 95.3%, 84% and 93.1% for awake, REM and NREM respectively on a group of patients from the sleep lab.
Year
DOI
Venue
2019
10.1007/978-3-030-32785-9_7
HOW AI IMPACTS URBAN LIVING AND PUBLIC HEALTH, ICOST 2019
Keywords
Field
DocType
Sleep stages, Transfer learning, BCG bed sensor, Deep learning
Pattern recognition,Convolutional neural network,Computer science,Non-rapid eye movement sleep,Computer network,Eye movement,Artificial intelligence,Deep learning,Cross-validation,Sleep Stages,Polysomnography,Ballistocardiography
Conference
Volume
ISSN
Citations 
11862
0302-9743
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Rayan Gargees100.34
James M. Keller23201436.69
Mihail Popescu346948.76
Marjorie Skubic41045105.36