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

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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]
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Database:	Engineering Source

Description
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]
ISSN:	20888708
DOI:	10.11591/ijece.v16i3.pp1562-1575