Abstract
Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is no mechanism to monitor a patient's progress with CPAP. Accurate detection of sleep stages from CPAP flow signal is crucial for such a mechanism. We propose, for the first time, an automated sleep staging model based only on the flow signal.Deep neural networks have recently shown high accuracy on sleep staging by eliminating handcrafted features. However, these methods focus exclusively on extracting informative features from the input signal, without paying much attention to the dynamics of sleep stages in the output sequence. We propose an end-to-end framework that uses a combination of deep convolution and recurrent neural networks to extract high-level features from raw flow signal with a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages. We improve upon the previous methods by 10% using our model, that can be augmented to the previous sleep staging deep learning methods. We also show that our method can be used to accurately track sleep metrics like sleep efficiency calculated from sleep stages that can be deployed for monitoring the response of CPAP therapy on sleep apnea patients. Apart from the technical contributions, we expect this study to motivate new research questions in sleep science.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 |
| Editors | Yang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1318-1327 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781538650356 |
| DOIs | |
| Publication status | Published - 22 Jan 2019 |
| Event | 2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States Duration: 10 Dec 2018 → 13 Dec 2018 |
Publication series
| Name | Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 |
|---|
Conference
| Conference | 2018 IEEE International Conference on Big Data, Big Data 2018 |
|---|---|
| Country | United States |
| City | Seattle |
| Period | 10/12/18 → 13/12/18 |
Fingerprint
Keywords
- Conditional Random Fields
- Sleep Staging
- Structured Prediction
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
Cite this
A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging. / Aggarwal, Karan; Khadanga, Swaraj; Rayhan Joty, Shafiq; Kazaglis, Louis; Srivastava, Jaideep.
Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. ed. / Yang Song; Bing Liu; Kisung Lee; Naoki Abe; Calton Pu; Mu Qiao; Nesreen Ahmed; Donald Kossmann; Jeffrey Saltz; Jiliang Tang; Jingrui He; Huan Liu; Xiaohua Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1318-1327 8622286 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging
AU - Aggarwal, Karan
AU - Khadanga, Swaraj
AU - Rayhan Joty, Shafiq
AU - Kazaglis, Louis
AU - Srivastava, Jaideep
PY - 2019/1/22
Y1 - 2019/1/22
N2 - Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is no mechanism to monitor a patient's progress with CPAP. Accurate detection of sleep stages from CPAP flow signal is crucial for such a mechanism. We propose, for the first time, an automated sleep staging model based only on the flow signal.Deep neural networks have recently shown high accuracy on sleep staging by eliminating handcrafted features. However, these methods focus exclusively on extracting informative features from the input signal, without paying much attention to the dynamics of sleep stages in the output sequence. We propose an end-to-end framework that uses a combination of deep convolution and recurrent neural networks to extract high-level features from raw flow signal with a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages. We improve upon the previous methods by 10% using our model, that can be augmented to the previous sleep staging deep learning methods. We also show that our method can be used to accurately track sleep metrics like sleep efficiency calculated from sleep stages that can be deployed for monitoring the response of CPAP therapy on sleep apnea patients. Apart from the technical contributions, we expect this study to motivate new research questions in sleep science.
AB - Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is no mechanism to monitor a patient's progress with CPAP. Accurate detection of sleep stages from CPAP flow signal is crucial for such a mechanism. We propose, for the first time, an automated sleep staging model based only on the flow signal.Deep neural networks have recently shown high accuracy on sleep staging by eliminating handcrafted features. However, these methods focus exclusively on extracting informative features from the input signal, without paying much attention to the dynamics of sleep stages in the output sequence. We propose an end-to-end framework that uses a combination of deep convolution and recurrent neural networks to extract high-level features from raw flow signal with a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages. We improve upon the previous methods by 10% using our model, that can be augmented to the previous sleep staging deep learning methods. We also show that our method can be used to accurately track sleep metrics like sleep efficiency calculated from sleep stages that can be deployed for monitoring the response of CPAP therapy on sleep apnea patients. Apart from the technical contributions, we expect this study to motivate new research questions in sleep science.
KW - Conditional Random Fields
KW - Sleep Staging
KW - Structured Prediction
UR - http://www.scopus.com/inward/record.url?scp=85062643165&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062643165&partnerID=8YFLogxK
U2 - 10.1109/BigData.2018.8622286
DO - 10.1109/BigData.2018.8622286
M3 - Conference contribution
T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
SP - 1318
EP - 1327
BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
A2 - Song, Yang
A2 - Liu, Bing
A2 - Lee, Kisung
A2 - Abe, Naoki
A2 - Pu, Calton
A2 - Qiao, Mu
A2 - Ahmed, Nesreen
A2 - Kossmann, Donald
A2 - Saltz, Jeffrey
A2 - Tang, Jiliang
A2 - He, Jingrui
A2 - Liu, Huan
A2 - Hu, Xiaohua
PB - Institute of Electrical and Electronics Engineers Inc.
ER -