نوع مقاله : علمی-پژوهشی
نویسندگان
1 دانشجوی کارشناسی، گروه مهندسی پزشکی، دانشکده مهندسی، دانشگاه بینالمللی امام رضا (ع)، مشهد، ایران
2 دانشجوی کارشناسی ارشد، گروه مهندسی پزشکی، دانشکده مهندسی، دانشگاه بینالمللی امام رضا (ع)، مشهد، ایران
3 گروه مهندسی پزشکی، دانشکده مهندسی، دانشگاه بین المللی امام رضا (ع)، مشهد، ایران.
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
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.
کلیدواژهها [English]
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