Individuality in Visual Representation of Pulse: A Comprehensive Analysis

Individuality, a foundational concept of traditional medicine, has been increasingly challenged by the standardization of modern healthcare. The emergence of wearable technology offers a scalable pathway toward precision health. Here, we explored the comprehensive visual representation of wearable photoplethysmography signals from a single device using the phase-amplitude coupling method. We demonstrated that the coupling diagrams contain visually distinguishable regions of physiological frequency components, including those consistent with reference breathing rates (R-squared value of 0.86 for linear regression and mean absolute error of 0.017 Hz, i.e., 1.02 breaths/min) and related to reference heart rates (R-squared value of 0.89). A similarity analysis at 0.1 Hz revealed a complex relationship between photoplethysmography signals and the derived skin sympathetic nerve activity and pulse rate variability. The numerical coupling matrices were further utilized to achieve cross-day and cross-session individual identification through machine-learning classifiers. The two-dimensional CNN classifier performed the best in the cross-session scenarios, achieving 90.37% accuracy with an equal error rate of 2.86%. This comprehensive analysis depicts the overall physiological information and individuality contained in digital pulse signals, providing conceptual and methodological foundations for future personalized health research.