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All Journal International Journal of Electrical and Computer Engineering Jurnal Informatika dan Teknik Elektro Terapan e-Jurnal Arus Elektro Indonesia RELE: Rekayasa Elektrikal dan Energi Jurnal Teknik Elektro Elsains : Jurnal Elektro Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Elektro dan Komputasi (ELKOM) Best : Journal of Applied Electrical, Science and Technology BEES: Bulletin of Electrical and Electronics Engineering Journal Geuthee of Engineering and Energy Jurnal Abdimas Terapan Jurnal Penelitian Multidisiplin Bangsa Emitor: Jurnal Teknik Elektro
Prenata, Giovanni Dimas
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Mechanical Engineering Physics Public Health Social Sciences Other

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Journal : International Journal of Electrical and Computer Engineering

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Performance analysis of single and multi-stage metaheuristic optimization on DFFNN for electrocardiogram-based emotion classification Prenata, Giovanni Dimas; Ridho’i, Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1562-1575

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 lassification 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 pproaches. 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 multi-stage 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.
Co-Authors Arief , Maulana Aris Heri Andriawan, Aris Heri Arshad, Mohd Rizal Baihaqi, Ahmad Basyarach, Niken Adriaty Fidhini, Fachrul Fauzan Noufanda, Yudo Farrel Patriadi , Andi Prasetyo, Arif Bayu Prayoga, Ageng Puji Slamet, Puji Rafli, Syah Achmad Amar Reza Sarwo Widagdo ridhoi, achmad Ridhoi, Ahmad Ridho’i, Ahmad Setiawan, Dani Agus Setiyawan, Ryan Agus Setyadjit , Kukuh Subekti Yuliananda, Subekti Subyakto, Doris Swarga, Lutfi Agung Syariffudin, Muhammad Alif Sofi Wardah, Izzah Aula