Research Article
Symbolic Fusion: A Novel Decision Support Algorithm for Sleep Staging Application
@ARTICLE{10.4108/eai.14-10-2015.2261933, author={Chen CHEN and Xue Liu and Adrien UGON and Xun ZHANG and Amara AMARA and Patrick GARDA and Jean-Gabriel GANASCIA and Carole PHILIPPE and Andrea PINNA}, title={Symbolic Fusion: A Novel Decision Support Algorithm for Sleep Staging Application}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={2}, number={8}, publisher={ACM}, journal_a={PHAT}, year={2015}, month={12}, keywords={symbolic fusion; decision support; sleep staging; polysomnography (psg)}, doi={10.4108/eai.14-10-2015.2261933} }- Chen CHEN
Xue Liu
Adrien UGON
Xun ZHANG
Amara AMARA
Patrick GARDA
Jean-Gabriel GANASCIA
Carole PHILIPPE
Andrea PINNA
Year: 2015
Symbolic Fusion: A Novel Decision Support Algorithm for Sleep Staging Application
PHAT
EAI
DOI: 10.4108/eai.14-10-2015.2261933
Abstract
With the rapid extension of clinical data and knowledge, decision making becomes a complex task for manual sleep staging. In this process, there is a need for integrating and analyzing information from heterogeneous data sources with high accuracy. This paper proposes a novel decision support algorithm—Symbolic Fusion for sleep staging application. The proposed algorithm provides high accuracy by combining data from heterogeneous sources, like EEG, EOG and EMG. This algorithm is developed for implementation in portable embedded systems for automatic sleep staging at low complexity and cost. The proposed algorithm proved to be an efficient design support method and achieved up to 76% overall agreement rate on our database of 12 patients.
Copyright © 2015 C. Chen et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
