Dimensionality Reduction Techniques for the Analysis of Physiological Signals in Human Sexual Response

This thesis explores the physiological signals that underlie the sexual response of women, focusing on the identification of the orgasm phase. Using data collected at Padova SexLab, Principal Component Analysis (PCA) was applied to reduce dimensionality, highlight latent structures, and investigate differences between experimental phases. The analysis revealed participant-specific heterogeneity, but also showed clustering patterns related to the experimental phases, suggesting that PCA can capture meaningful variations in physiological responses. Additional exploration, combining data from the same phase among participants, indicated shared patterns, emphasizing the potential of multivariate analysis to uncover consistent physiological trends. In general, the study provides preliminary information on the sexual physiology of women and demonstrates the utility of PCA as an exploratory tool for future research.