View in EDS

Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification.

Saved in:
Bibliographic Details
Title: Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification.
Authors: Hilal, Anwer Mustafa1 a.hilal@psau.edu.sa, Al-Rasheed, Amal2, Alzahrani, Jaber S.3, Eltahir, Majdy M.4, Al Duhayyim, Mesfer5, Salem, Nermin M.6, Yaseen, Ishfaq1, Motwakel, Abdelwahed1
Source: Computer Systems Science & Engineering. 2023, Vol. 45 Issue 2, p1249-1263. 15p.
Subjects: Sleep stages, Deep learning, Machine learning, Electroencephalography, Computer simulation
Abstract: Sleep plays a vital role in optimum working of the brain and the body. Numerous people suffer from sleep-oriented illnesses like apnea, insomnia, etc. Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording. Sleep stage scoring is mainly based on experts' knowledge which is laborious and time consuming. Hence, it can be essential to design automated sleep stage classification model using machine learning (ML) and deep learning (DL) approaches. In this view, this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification (CMVODL-SSC) model using Electroencephalogram (EEG) signals. The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals. Primarily, data pre-processing is performed to convert the actual data into useful format. Besides, a cascaded long short term memory (CLSTM) model is employed to perform classification process. At last, the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model. In order to report the enhancements of the CMVODL-SSC model, a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%. [ABSTRACT FROM AUTHOR]
Copyright of Computer Systems Science & Engineering is the property of Tech Science Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 161541164
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Hilal%2C+Anwer+Mustafa%22">Hilal, Anwer Mustafa</searchLink><relatesTo>1</relatesTo><i> a.hilal@psau.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Al-Rasheed%2C+Amal%22">Al-Rasheed, Amal</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Alzahrani%2C+Jaber+S%2E%22">Alzahrani, Jaber S.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Eltahir%2C+Majdy+M%2E%22">Eltahir, Majdy M.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Al+Duhayyim%2C+Mesfer%22">Al Duhayyim, Mesfer</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Salem%2C+Nermin+M%2E%22">Salem, Nermin M.</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22Yaseen%2C+Ishfaq%22">Yaseen, Ishfaq</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Motwakel%2C+Abdelwahed%22">Motwakel, Abdelwahed</searchLink><relatesTo>1</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Computer+Systems+Science+%26+Engineering%22">Computer Systems Science & Engineering</searchLink>. 2023, Vol. 45 Issue 2, p1249-1263. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Sleep+stages%22">Sleep stages</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Sleep plays a vital role in optimum working of the brain and the body. Numerous people suffer from sleep-oriented illnesses like apnea, insomnia, etc. Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording. Sleep stage scoring is mainly based on experts' knowledge which is laborious and time consuming. Hence, it can be essential to design automated sleep stage classification model using machine learning (ML) and deep learning (DL) approaches. In this view, this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification (CMVODL-SSC) model using Electroencephalogram (EEG) signals. The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals. Primarily, data pre-processing is performed to convert the actual data into useful format. Besides, a cascaded long short term memory (CLSTM) model is employed to perform classification process. At last, the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model. In order to report the enhancements of the CMVODL-SSC model, a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Systems Science & Engineering is the property of Tech Science Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=161541164
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.32604/csse.2023.030603
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 1249
    Subjects:
      – SubjectFull: Sleep stages
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Electroencephalography
        Type: general
      – SubjectFull: Computer simulation
        Type: general
    Titles:
      – TitleFull: Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Hilal, Anwer Mustafa
      – PersonEntity:
          Name:
            NameFull: Al-Rasheed, Amal
      – PersonEntity:
          Name:
            NameFull: Alzahrani, Jaber S.
      – PersonEntity:
          Name:
            NameFull: Eltahir, Majdy M.
      – PersonEntity:
          Name:
            NameFull: Al Duhayyim, Mesfer
      – PersonEntity:
          Name:
            NameFull: Salem, Nermin M.
      – PersonEntity:
          Name:
            NameFull: Yaseen, Ishfaq
      – PersonEntity:
          Name:
            NameFull: Motwakel, Abdelwahed
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: 2023
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 02676192
          Numbering:
            – Type: volume
              Value: 45
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Computer Systems Science & Engineering
              Type: main
ResultId 1