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September 19, 2025RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218Open Access

Stress Classification Using Physiological Signals: A Comprehensive Review of Methods and Approaches Combined With a Novel CNN-Based Ecg Experiment

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Authors

CRClarissa Garcia RodriguesSRSandro José RigoKMK. M. Mark

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Overview

This review highlights challenges in stress classification using physiological signals, emphasizing personalized models and real-time applications.

Key Points

  • The use of physiological signals for stress classification shows a promising avenue for healthcare advancements.
  • A novel CNN architecture for classifying ECG signals achieved 60.95% accuracy on an independent test set.
  • Developing personalized models is crucial to enhance stress classification in real-world applications.
  • Small datasets limit the accuracy of stress classification methods, calling for innovative solutions.

Cite This Study

Rodrigues et al. (2025) studied this question.

synapsesocial.com/papers/68d464ff31b076d99fa64c7bhttps://doi.org/10.47820/recima21.v6i9.6745
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