Imperial College London
Alternate

Professor Paul M. Matthews

Faculty of Medicine, Department of Brain Sciences

Edmond and Lily Safra Chair, Head of Department
 
 
 
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Contact

 

+44 (0)20 7594 2855p.matthews

 
 
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Assistant

 

Ms Siobhan Dillon +44 (0)20 7594 2855

 
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Location

 

E502Burlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Dong:2017:10.1109/TNSRE.2017.2733220,
author = {Dong, H and Supratak, A and Pan, W and Wu, C and Matthews, PM and Guo, Y},
doi = {10.1109/TNSRE.2017.2733220},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
pages = {324--333},
title = {Mixed neural network approach for temporal sleep stage classification},
url = {http://dx.doi.org/10.1109/TNSRE.2017.2733220},
volume = {26},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage classification. EEG-based characterizations of sleep stage progression contribute the diagnosis and monitoring of the many pathologies of sleep. Several prior reports explored ways of automating the analysis of sleep EEG and of reducing the complexity of the data needed for reliable discrimination of sleep stages at lower cost in the home. However, these reports have involved recordings from electrodes placed on the cranial vertex or occiput, which are both uncomfortable and difficult to position. Previous studies of sleep stage scoring that used only frontal electrodes with a hierarchical decision tree motivated this paper, in which we have taken advantage of rectifier neural network for detecting hierarchical features and long short-term memory (LSTM) network for sequential data learning to optimize classification performance with single-channel recordings. After exploring alternative electrode placements, we found a comfortable configuration of a single-channel EEG on the forehead and have shown that it can be integrated with additional electrodes for simultaneous recording of the electrooculogram (EOG). Evaluation of data from 62 people (with 494 hours sleep) demonstrated better performance of our analytical algorithm than is available from existing approaches with vertex or occipital electrode placements. Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.
AU  - Dong,H
AU  - Supratak,A
AU  - Pan,W
AU  - Wu,C
AU  - Matthews,PM
AU  - Guo,Y
DO  - 10.1109/TNSRE.2017.2733220
EP  - 333
PY  - 2017///
SN  - 1534-4320
SP  - 324
TI  - Mixed neural network approach for temporal sleep stage classification
T2  - IEEE Transactions on Neural Systems and Rehabilitation Engineering
UR  - http://dx.doi.org/10.1109/TNSRE.2017.2733220
UR  - http://hdl.handle.net/10044/1/54547
VL  - 26
ER  -
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