Open Access

ARTICLE

Enhanced Multimodal Physiological Signal Analysis for Pain Assessment Using Optimized Ensemble Deep Learning

Karim Gasmi¹, Olfa Hrizi^1,*, Najib Ben Aoun^2,3, Ibrahim Alrashdi¹, Ali Alqazzaz⁴, Omer Hamid⁵, Mohamed O. Altaieb¹, Alameen E. M. Abdalrahman¹, Lassaad Ben Ammar⁶, Manel Mrabet⁶, Omrane Necibi¹

1 Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia
2 Faculty of Computing and Information, Al-Baha University, Al-Baha, 65528, Saudi Arabia
3 REGIM-Lab, National School of Engineers of Sfax, University of Sfax, Sfax, 3038, Tunisia
4 College of Computing and Information Technology, University of Bisha, Bisha, 61922, Saudi Arabia
5 Cybersecurity Department, College of Engineering and Information Technology, Buraydah Private Colleges, Buraydah, 51418, Saudi Arabia
6 Department of Computer Science, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia

* Corresponding Author: Olfa Hrizi. Email:

(This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)

Computer Modeling in Engineering & Sciences 2025, 143(2), 2459-2489. https://doi.org/10.32604/cmes.2025.065817

Received 22 March 2025; Accepted 30 April 2025; Issue published 30 May 2025

Abstract

The potential applications of multimodal physiological signals in healthcare, pain monitoring, and clinical decision support systems have garnered significant attention in biomedical research. Subjective self-reporting is the foundation of conventional pain assessment methods, which may be unreliable. Deep learning is a promising alternative to resolve this limitation through automated pain classification. This paper proposes an ensemble deep-learning framework for pain assessment. The framework makes use of features collected from electromyography (EMG), skin conductance level (SCL), and electrocardiography (ECG) signals. We integrate Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Bidirectional Gated Recurrent Units (BiGRU), and Deep Neural Networks (DNN) models. We then aggregate their predictions using a weighted averaging ensemble technique to increase the classification’s robustness. To improve computing efficiency and remove redundant features, we use Particle Swarm Optimization (PSO) for feature selection. This enables us to reduce the features’ dimensionality without sacrificing the classification’s accuracy. With improved accuracy, precision, recall, and F1-score across all pain levels, the experimental results show that the suggested ensemble model performs better than individual deep learning classifiers. In our experiments, the suggested model achieved over 98% accuracy, suggesting promising automated pain assessment performance. However, due to differences in validation protocols, comparisons with previous studies are still limited. Combining deep learning and feature selection techniques significantly improves model generalization, reducing overfitting and enhancing classification performance. The evaluation was conducted using the BioVid Heat Pain Dataset, confirming the model’s effectiveness in distinguishing between different pain intensity levels.

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