Asymmetric measures of polar Chebyshev chaotic map for discrete/dimensional emotion recognition using PPG

Abstract

Human emotion recognition is a challenge in both medical and non-medical fields. While there are several physiological signals available for detecting emotions, the Photoplethysmogram (PPG) signal has been disregarded, despite its non-invasive nature and ease of recording using common devices like smartphones. This study addresses the research question: How can we effectively analyze PPG signals to enhance emotion recognition? To tackle this question, we employed novel nonlinear measures based on the polar form of the Chebyshev chaotic map to analyze the dynamic behavior of PPG signals. What makes this research unique is the use of the polar form of the Chebyshev chaotic map for biosignal analysis, specifically in quantifying the map’s asymmetry using multiple indices. Our main goal is to improve our understanding of the nonlinear characteristics of PPG signals and gain new insights into their dynamic properties. While previous research has explored PPG-based emotion recognition, our study stands out by utilizing the Chebyshev chaotic map to characterize PPG signals. We evaluated the effectiveness of these features in discrete and dimensional emotion recognition tasks, using the Database for Emotion Analysis using Physiological signals (DEAP) as a benchmark. Our experimental results showed a maximum accuracy of 94.49% for discrete emotion recognition and 85.59% for dimensional emotion recognition. Experiments were also conducted on another public dataset to further validate the efficacy of the proposed approach. The significance of our study lies in providing new insights into the nonlinear characteristics of PPG signals and enhancing emotion recognition capabilities, thereby contributing to both the fields of emotional analysis and biomedical engineering.