Loading [MathJax]/extensions/MathMenu.js
Electrooculogram based sleep stage classification using deep belief network | IEEE Conference Publication | IEEE Xplore

Electrooculogram based sleep stage classification using deep belief network


Abstract:

In this work, we used single electrooculogram (EOG) signal to perform automatic sleep scoring. Deep belief network (DBN) and combination of DBN and Hidden Markov Models (...Show More

Abstract:

In this work, we used single electrooculogram (EOG) signal to perform automatic sleep scoring. Deep belief network (DBN) and combination of DBN and Hidden Markov Models (HMM) are employed to discriminate sleep stages. Under the leave-one-out protocol, the average accuracy of DBN and DBN-HMM are 77.7% and 83.3% for all sleep stages, respectively. On the other hand, we found the EOG signal not only contribute to identify stages of Awake and rapid eye movement, also contribute to discriminate stage 2 and slow wave sleep stage.
Date of Conference: 12-17 July 2015
Date Added to IEEE Xplore: 01 October 2015
ISBN Information:

ISSN Information:

Conference Location: Killarney

Contact IEEE to Subscribe

References

References is not available for this document.