View in EDS PDF Full Text

Performance analysis of single and multi-stage metaheuristic optimization on DFFNN for electrocardiogram-based emotion classification.

Saved in:
Bibliographic Details
Title: Performance analysis of single and multi-stage metaheuristic optimization on DFFNN for electrocardiogram-based emotion classification.
Authors: Prenata, Giovanni Dimas1 gprenata@untag-sby.ac.id, Ridho'i, Ahmad1 ridhoi@untag-sby.ac.id
Source: International Journal of Electrical & Computer Engineering (2088-8708). Jun2026, Vol. 16 Issue 3, p1562-1575. 14p.
Subjects: Feedforward neural networks, Metaheuristic algorithms, Genetic algorithms, Electrocardiography, Grey Wolf Optimizer algorithm, Particle swarm optimization, Emotion recognition
Abstract: Emotion classification based on electrocardiogram (ECG) signals has attracted increasing attention in affective computing and biomedical signal processing. However, training deep feedforward neural networks (DFFNN) using conventional gradient-based learning often suffers from local minima and slow convergence, particularly when dealing with nonlinear and limited datasets. This study presents a comprehensive performance analysis of single-stage and multi-stage metaheuristic optimization strategies applied to DFFNN for ECG-based emotion classification in elderly participants. Five models were evaluated: Pure DFFNN, DFFNN optimized using genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), and a hybrid multi-stage DFFNN+GA+GWO model. Experimental results from six independent trials demonstrate a substantial reduction in mean squared error (MSE) when metaheuristic optimization is applied. Pure DFFNN produced final MSE values in the range of 0.07462-0.08977, whereas DFFNN+GWO reduced MSE to 0.01894-0.02411. The proposed multi-stage DFFNN+GA+GWO achieved the lowest MSE of 0.014286 in the best run and an average MSE of approximately 0.0212 across trials. Training accuracy improved from 57.14%-66.67% (Pure DFFNN) to 80.95%-85.71% using metaheuristic approaches. Although testing accuracy remained relatively stable at 33.33%-50.00% due to dataset size constraints, convergence behavior analysis shows that multi-stage optimization enhances stability and reduces oscillatory updates. These findings confirm that multistage metaheuristic optimization significantly improves training stability and error minimization in DFFNN models, offering a promising strategy for robust ECG-based emotion classification under small-sample conditions. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science 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 Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 194285641
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Performance analysis of single and multi-stage metaheuristic optimization on DFFNN for electrocardiogram-based emotion classification.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Prenata%2C+Giovanni+Dimas%22">Prenata, Giovanni Dimas</searchLink><relatesTo>1</relatesTo><i> gprenata@untag-sby.ac.id</i><br /><searchLink fieldCode="AR" term="%22Ridho'i%2C+Ahmad%22">Ridho'i, Ahmad</searchLink><relatesTo>1</relatesTo><i> ridhoi@untag-sby.ac.id</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Jun2026, Vol. 16 Issue 3, p1562-1575. 14p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Feedforward+neural+networks%22">Feedforward neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Electrocardiography%22">Electrocardiography</searchLink><br /><searchLink fieldCode="DE" term="%22Grey+Wolf+Optimizer+algorithm%22">Grey Wolf Optimizer algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Emotion+recognition%22">Emotion recognition</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Emotion classification based on electrocardiogram (ECG) signals has attracted increasing attention in affective computing and biomedical signal processing. However, training deep feedforward neural networks (DFFNN) using conventional gradient-based learning often suffers from local minima and slow convergence, particularly when dealing with nonlinear and limited datasets. This study presents a comprehensive performance analysis of single-stage and multi-stage metaheuristic optimization strategies applied to DFFNN for ECG-based emotion classification in elderly participants. Five models were evaluated: Pure DFFNN, DFFNN optimized using genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), and a hybrid multi-stage DFFNN+GA+GWO model. Experimental results from six independent trials demonstrate a substantial reduction in mean squared error (MSE) when metaheuristic optimization is applied. Pure DFFNN produced final MSE values in the range of 0.07462-0.08977, whereas DFFNN+GWO reduced MSE to 0.01894-0.02411. The proposed multi-stage DFFNN+GA+GWO achieved the lowest MSE of 0.014286 in the best run and an average MSE of approximately 0.0212 across trials. Training accuracy improved from 57.14%-66.67% (Pure DFFNN) to 80.95%-85.71% using metaheuristic approaches. Although testing accuracy remained relatively stable at 33.33%-50.00% due to dataset size constraints, convergence behavior analysis shows that multi-stage optimization enhances stability and reduces oscillatory updates. These findings confirm that multistage metaheuristic optimization significantly improves training stability and error minimization in DFFNN models, offering a promising strategy for robust ECG-based emotion classification under small-sample conditions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science 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=194285641
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.11591/ijece.v16i3.pp1562-1575
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 1562
    Subjects:
      – SubjectFull: Feedforward neural networks
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Electrocardiography
        Type: general
      – SubjectFull: Grey Wolf Optimizer algorithm
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Emotion recognition
        Type: general
    Titles:
      – TitleFull: Performance analysis of single and multi-stage metaheuristic optimization on DFFNN for electrocardiogram-based emotion classification.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Prenata, Giovanni Dimas
      – PersonEntity:
          Name:
            NameFull: Ridho'i, Ahmad
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20888708
          Numbering:
            – Type: volume
              Value: 16
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708)
              Type: main
ResultId 1