Sleep Stage Classification Using Dempster–Shafer Theory for Classifier Fusion

Mehdi Abdollahpour; Tohid Yousefi Rezaii; Ali Farzamnia; Saeed Meshgini

doi:10.1109/IICAIET.2018.8638445

Sleep Stage Classification Using Dempster–Shafer Theory for Classifier Fusion

Mehdi Abdollahpour, Tohid Yousefi Rezaii, Ali Farzamnia, Saeed Meshgini

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

7 Citations (Scopus)

Abstract

The importance of the automatic sleep stage classification is increasing in order to study of sleep stage transitions and sleep health. The researches are introducing new methods to obtain the highest accuracy compared to the expert scored hypnograms through classification of the electroencephalogram (EEG). Because of limitations when using the single information source this article combines the outcomes of classifiers obtained from different sources. By extracting 13 features from two channels of EEG signal and using these features for learning and testing linear discriminant analysis classifier, we reached accuracy of 71.93% for the Fpz-Cz channel and 6S.33% for the Pz-Oz channel. In the last step, Dempster-Shafer theory of evidence is used for classifier fusion and combining the outputs derived from the classification of two EEG channels, the classification accuracy increased by 88.23.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	32-35
Number of pages	4
ISBN (Electronic)	9781538678138, 9781538678121
ISBN (Print)	9781538678145
DOIs	https://doi.org/10.1109/IICAIET.2018.8638445
Publication status	Published - 10 Feb 2019
Externally published	Yes
Event	2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology - Kota Kinabalu, Sabah, Malaysia Duration: 8 Nov 2018 → 8 Nov 2018

Conference

Conference	2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology
Abbreviated title	IICAIET 2018
Country/Territory	Malaysia
City	Kota Kinabalu, Sabah
Period	8/11/18 → 8/11/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/IICAIET.2018.8638445Licence: Unspecified

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Abdollahpour, M., Rezaii, T. Y., Farzamnia, A., & Meshgini, S. (2019). Sleep Stage Classification Using Dempster–Shafer Theory for Classifier Fusion. In Proceedings - 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2018 (pp. 32-35). [8638445] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IICAIET.2018.8638445

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title = "Sleep Stage Classification Using Dempster–Shafer Theory for Classifier Fusion",

abstract = "The importance of the automatic sleep stage classification is increasing in order to study of sleep stage transitions and sleep health. The researches are introducing new methods to obtain the highest accuracy compared to the expert scored hypnograms through classification of the electroencephalogram (EEG). Because of limitations when using the single information source this article combines the outcomes of classifiers obtained from different sources. By extracting 13 features from two channels of EEG signal and using these features for learning and testing linear discriminant analysis classifier, we reached accuracy of 71.93% for the Fpz-Cz channel and 6S.33% for the Pz-Oz channel. In the last step, Dempster-Shafer theory of evidence is used for classifier fusion and combining the outputs derived from the classification of two EEG channels, the classification accuracy increased by 88.23.",

keywords = "Classifier fusion, Dempster-Shafer theory of evidence, EEG, Sleep stage classification",

author = "Mehdi Abdollahpour and Rezaii, {Tohid Yousefi} and Ali Farzamnia and Saeed Meshgini",

note = "Funding Information: The authors appreciate those who contributed to make this research successful. This research is under support of Center for Research and Innovation (PPI) and Faculty of Engineering, Universiti Malaysia Sabah (UMS) under the Fundamental Research Grant (FRG0453-TK-1/2016). Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2018 ; Conference date: 08-11-2018 Through 08-11-2018",

year = "2019",

month = feb,

day = "10",

doi = "10.1109/IICAIET.2018.8638445",

language = "English",

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pages = "32--35",

booktitle = "Proceedings - 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2018",

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}

Abdollahpour, M, Rezaii, TY, Farzamnia, A & Meshgini, S 2019, Sleep Stage Classification Using Dempster–Shafer Theory for Classifier Fusion. in Proceedings - 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2018., 8638445, Institute of Electrical and Electronics Engineers Inc., pp. 32-35, 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology, Kota Kinabalu, Sabah, Malaysia, 8/11/18. https://doi.org/10.1109/IICAIET.2018.8638445

Sleep Stage Classification Using Dempster–Shafer Theory for Classifier Fusion. / Abdollahpour, Mehdi; Rezaii, Tohid Yousefi; Farzamnia, Ali et al.

Proceedings - 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 32-35 8638445.

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

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AU - Abdollahpour, Mehdi

AU - Rezaii, Tohid Yousefi

AU - Farzamnia, Ali

AU - Meshgini, Saeed

N1 - Funding Information: The authors appreciate those who contributed to make this research successful. This research is under support of Center for Research and Innovation (PPI) and Faculty of Engineering, Universiti Malaysia Sabah (UMS) under the Fundamental Research Grant (FRG0453-TK-1/2016). Publisher Copyright: © 2018 IEEE.

PY - 2019/2/10

Y1 - 2019/2/10

N2 - The importance of the automatic sleep stage classification is increasing in order to study of sleep stage transitions and sleep health. The researches are introducing new methods to obtain the highest accuracy compared to the expert scored hypnograms through classification of the electroencephalogram (EEG). Because of limitations when using the single information source this article combines the outcomes of classifiers obtained from different sources. By extracting 13 features from two channels of EEG signal and using these features for learning and testing linear discriminant analysis classifier, we reached accuracy of 71.93% for the Fpz-Cz channel and 6S.33% for the Pz-Oz channel. In the last step, Dempster-Shafer theory of evidence is used for classifier fusion and combining the outputs derived from the classification of two EEG channels, the classification accuracy increased by 88.23.

AB - The importance of the automatic sleep stage classification is increasing in order to study of sleep stage transitions and sleep health. The researches are introducing new methods to obtain the highest accuracy compared to the expert scored hypnograms through classification of the electroencephalogram (EEG). Because of limitations when using the single information source this article combines the outcomes of classifiers obtained from different sources. By extracting 13 features from two channels of EEG signal and using these features for learning and testing linear discriminant analysis classifier, we reached accuracy of 71.93% for the Fpz-Cz channel and 6S.33% for the Pz-Oz channel. In the last step, Dempster-Shafer theory of evidence is used for classifier fusion and combining the outputs derived from the classification of two EEG channels, the classification accuracy increased by 88.23.

KW - Classifier fusion

KW - Dempster-Shafer theory of evidence

KW - EEG

KW - Sleep stage classification

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BT - Proceedings - 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2018

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T2 - 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology

Y2 - 8 November 2018 through 8 November 2018

ER -

Sleep Stage Classification Using Dempster–Shafer Theory for Classifier Fusion

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