University of Hertfordshire

By the same authors

Improving sleep stage classification from electroencephalographic signals by fusion of contextual information

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Original languageEnglish
Title of host publication2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467379830
DOIs
Publication statusPublished - 28 Dec 2015
Event15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015 - Belgrade, Serbia
Duration: 2 Nov 20154 Nov 2015

Conference

Conference15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015
CountrySerbia
CityBelgrade
Period2/11/154/11/15

Abstract

In this article we present a fusion architecture for the automatic classification of sleep stages. The architecture relies on time and frequency domain features which are processed by dissimilar classifiers. The initial predictions of each classifier are refined by using fusion of the prediction estimations together with temporal contextual information of the electroencephalographic signal. The experimental results showed that the proposed architecture achieved approximately 95% sleep stage classification accuracy, which corresponds to an improvement of 5% comparing to the best performing single classifier.

ID: 11449189