Skip to main content

ORIGINAL RESEARCH article

Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1486763

An Emotion Recognition Method based on Frequency-domain Features of PPG

Provisionally accepted
Zhibin Zhu Zhibin Zhu 1Xuanyi Wang Xuanyi Wang 2Yifei Xu Yifei Xu 1*Wanlin Chen Wanlin Chen 2Jing Zheng Jing Zheng 1Shulin Chen Shulin Chen 2*Hang Chen Hang Chen 1*
  • 1 College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
  • 2 Department of Psychology and Behavioral Sciences, Graduate School, Zhejiang University, Hangzhou, Zhejiang Province, China

The final, formatted version of the article will be published soon.

    Objective: This study aims to employ physiological model simulation to systematically analyze the frequency-domain components of PPG signals and extract their key features. The efficacy of these frequency-domain features in effectively distinguishing emotional states will also be investigated.: A dual windkessel model was employed to analyze PPG signal frequency components and extract distinctive features. Experimental data collection encompassed both physiological (PPG) and psychological measurements, with subsequent analysis involving distribution patterns and statistical testing (U-tests) to examine feature-emotion relationships. The study implemented support vector machine (SVM) classification to evaluate feature effectiveness, complemented by comparative analysis using pulse rate variability (PRV) features, morphological features, and the DEAP dataset. Results: The results demonstrate significant differentiation in PPG frequency-domain feature responses to arousal and valence variations, achieving classification accuracies of 87.5% and 81.4% respectively. Validation on the DEAP dataset yielded consistent patterns with accuracies of 73.5% (arousal) and 71.5% (valence). Feature fusion incorporating the proposed frequency-domain features enhanced classification performance, surpassing 90% accuracy. Conclusion: This study uses physiological modeling to analyze PPG signal frequency components and extract key features. We evaluate their effectiveness in emotion recognition and reveal relationships among physiological parameters, frequency features, and emotional states Significance: These findings advance understanding of emotion recognition mechanisms and provide a foundation for future research.

    Keywords: Photoplethysmography (PPG), emotion identification, support vector machine (SVM), PPG Frequency-Domian Analysis, Dual Windkessel Model

    Received: 29 Aug 2024; Accepted: 29 Jan 2025.

    Copyright: © 2025 Zhu, Wang, Xu, Chen, Zheng, Chen and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yifei Xu, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
    Shulin Chen, Department of Psychology and Behavioral Sciences, Graduate School, Zhejiang University, Hangzhou, 310028, Zhejiang Province, China
    Hang Chen, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.