Exploring Gender-Related Variations in Photoplethysmography

Photoplethysmographic signal (PPG) analysis is emerging in healthcare applications due to its affordable cost and noninvasiveness. However, it is well known how PPG is influenced by several factors, potentially including the gender of the subject. This study aims to identify which parameters of the PPG signal show variations in relation to gender. We used a machine learning approach to classify the gender of subjects using a mathematical model of the PPG signal. In a cross-validation procedure, our method correctly classified 90 out of 115 subjects (78\%). Heart cycle and systolic phase duration, along with variables related to the reflected wave of the PPG signal, emerged as significant parameters. These findings enhance our understanding of gender-related PPG variability, offering potential insights for future clinical applications in cardiovascular monitoring.