Abstract
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. METHODS: 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.