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

Iosif Mporas; Anastasia Efstathiou; Vasileios Megalooikonomou

doi:10.1109/BIBE.2015.7367736

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

Iosif Mporas, Anastasia Efstathiou, Vasileios Megalooikonomou

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	9781467379830
DOIs	https://doi.org/10.1109/BIBE.2015.7367736
Publication status	Published - 28 Dec 2015
Event	15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015 - Belgrade, Serbia Duration: 2 Nov 2015 → 4 Nov 2015

Conference

Conference	15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015
Country/Territory	Serbia
City	Belgrade
Period	2/11/15 → 4/11/15

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10.1109/BIBE.2015.7367736

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title = "Improving sleep stage classification from electroencephalographic signals by fusion of contextual information",

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.",

author = "Iosif Mporas and Anastasia Efstathiou and Vasileios Megalooikonomou",

year = "2015",

month = dec,

day = "28",

doi = "10.1109/BIBE.2015.7367736",

language = "English",

booktitle = "2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

note = "15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015 ; Conference date: 02-11-2015 Through 04-11-2015",

}

Mporas, I, Efstathiou, A & Megalooikonomou, V 2015, Improving sleep stage classification from electroencephalographic signals by fusion of contextual information. in 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015., 7367736, Institute of Electrical and Electronics Engineers (IEEE), 15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015, Belgrade, Serbia, 2/11/15. https://doi.org/10.1109/BIBE.2015.7367736

Improving sleep stage classification from electroencephalographic signals by fusion of contextual information. / Mporas, Iosif; Efstathiou, Anastasia; Megalooikonomou, Vasileios.

2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015. Institute of Electrical and Electronics Engineers (IEEE), 2015. 7367736.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Mporas, Iosif

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AU - Megalooikonomou, Vasileios

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N2 - 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.

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M3 - Conference contribution

AN - SCOPUS:84962799363

BT - 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015

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Y2 - 2 November 2015 through 4 November 2015

ER -

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

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