research-article

A Hybrid Approach for Sleep Stages Classification

Authors:

Abdelhamid Ouanes,

Lilia RejebAuthors Info & Claims

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

Pages 493 - 500

https://doi.org/10.1145/2908812.2908910

Published: 20 July 2016 Publication History

Abstract

Healthy sleep is essential for human well-being. Sleep analysis is a necessary process for the majority of sleep disorders diagnosis. In this work we propose to analyze brain activity through Electroencephalogram analysis in order to identify sleep stages variation. We focus on the classification phase. Most works in sleep stages classification are based on prior experts signal scoring which is a hard task. So many available unlabeled data remain unused. To explore more these data and enrich the study of sleep classification, we propose a hybrid approach based on learning classifier systems and artificial neural networks. The effectiveness of the proposed approach was investigated using real electroencephalography data. Good results were reached comparing to supervised learning methods usually used. The proposed approach provides also, an explicit model that could be analyzed a posteriori by experts.

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Cited By

Haj brahim AMorched SBoujelbene Y(2021)How formal institutional antecedents affect Tunisian venture creation decision scriptsJournal of Global Entrepreneurship Research10.1007/s40497-021-00267-011:1(421-438)Online publication date: 23-Jun-2021
https://doi.org/10.1007/s40497-021-00267-0
Ferjani RRejeb LSaid L(2021)Belief eXtended Classifier System: A New Approach for Dealing with Uncertainty in Sleep Stages ClassificationHybrid Intelligent Systems10.1007/978-3-030-73050-5_46(454-463)Online publication date: 17-Apr-2021
https://doi.org/10.1007/978-3-030-73050-5_46
Ferjani RRejeb LSaid L(2020)Cooperative Reinforcement Multi-Agent Learning System for Sleep Stages Classification2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA)10.1109/OCTA49274.2020.9151700(1-8)Online publication date: Feb-2020
https://doi.org/10.1109/OCTA49274.2020.9151700
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Index Terms

A Hybrid Approach for Sleep Stages Classification
1. Applied computing
  1. Life and medical sciences
    1. Health informatics

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cover image ACM Conferences

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

July 2016

1196 pages

ISBN:9781450342063

DOI:10.1145/2908812

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2016

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GECCO '16

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SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Paper Acceptance Rate 137 of 381 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Haj brahim AMorched SBoujelbene Y(2021)How formal institutional antecedents affect Tunisian venture creation decision scriptsJournal of Global Entrepreneurship Research10.1007/s40497-021-00267-011:1(421-438)Online publication date: 23-Jun-2021
https://doi.org/10.1007/s40497-021-00267-0
Ferjani RRejeb LSaid L(2021)Belief eXtended Classifier System: A New Approach for Dealing with Uncertainty in Sleep Stages ClassificationHybrid Intelligent Systems10.1007/978-3-030-73050-5_46(454-463)Online publication date: 17-Apr-2021
https://doi.org/10.1007/978-3-030-73050-5_46
Ferjani RRejeb LSaid L(2020)Cooperative Reinforcement Multi-Agent Learning System for Sleep Stages Classification2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA)10.1109/OCTA49274.2020.9151700(1-8)Online publication date: Feb-2020
https://doi.org/10.1109/OCTA49274.2020.9151700
Ferjani RRejeb LSaid L(2020)Unsupervised Sleep Stages Classification Based on Physiological SignalsAdvances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection10.1007/978-3-030-49778-1_11(134-145)Online publication date: 15-Jun-2020
https://doi.org/10.1007/978-3-030-49778-1_11

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