Evaluation of Nonlinear Characteristics in Multi-Wavelength Photoplethysmography Signals for Muscle and Skin Sympathetic Nerve Activity in Stress Classification

Articles in Press

Document Type : Original Article

Authors

1 Bachelor’s student, Department of Biomedical Engineering, Faculty of Engineering, Imam Reza International University, Mashhad, Iran

2 Master's student, Department of Biomedical Engineering, Faculty of Engineering, Imam Reza International University, Mashhad, Iran

3 Department of Biomedical Engineering, Faculty of Engineering, Imam Reza International University, Mashhad, Iran

10.22034/tjee.2026.68024.5044

Abstract

Stress is one of the common psychological challenges of the 21st century that affects the function of the autonomic nervous system. In this study, the feasibility of non-invasive monitoring of muscle sympathetic nerve activity (MSNA) and skin sympathetic nerve activity (SSNA) was investigated using pre-extracted nonlinear features from multi-wavelength photoplethysmography (PPG) signals and by employing machine learning algorithms. PPG data from 32 healthy individuals (19 to 38 years old) at four wavelengths (red, infrared, blue, and green) were analyzed during three time phases (pre-stress, during stress, and post-stress induced by handgrip and cold pressor tests). Nonlinear features, including Higuchi, Katz, and Petrosian fractal dimensions, approximate entropy, and sample entropy, were used for classification. These features were applied in three classifiers—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Recurrent Neural Network (RNN)—to classify stress states. The highest performance was observed using approximate entropy at the blue wavelength when distinguishing the pre-stress phase from the first two minutes of stress in the handgrip test (SVM: accuracy = 90.96%, AUC = 1.00). Independent t-tests and Wilcoxon tests revealed significant differences (p < 0.05) in the blue, green, and infrared wavelengths. These results confirm the role of nonlinear features and optimal wavelength selection in effective stress monitoring and highlight the potential application of PPG as a low-cost, non-invasive tool.

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Tabriz Journal of Electrical Engineering

Articles in Press, Corrected Proof
Available Online from 11 April 2026

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