Temporal complexity in photoplethysmography and its influence on blood pressure - Xing, Xiaoman; Huang, Rui; Hao, Liling; Jiang, Chenyu; Dong, Wen-Fei (Frontiers in Physiology, 2023)
The temporal complexity of photoplethysmography (PPG) provides valuable information about blood pressure (BP). In this study, we aim to interpret the stochastic PPG patterns with a modelbased simulation, which may help optimize the BP estimation algorithms.The classic four-element Windkessel model is adapted in this study to incorporate BPdependent compliance profiles. Simulations are performed to generate PPG responses to pulse and continuous stimuli at various timescales, aiming to mimic sudden or gradual hemodynamic changes observed in real-life scenarios. To quantify the temporal complexity of PPG, we utilize the Higuchi fractal dimension (HFD) and autocorrelation function (ACF). These measures provide insights into the intricate temporal patterns exhibited by PPG. To validate the simulation results, continuous recordings of BP, PPG, and stroke volume from 40 healthy subjects were used.Pulse simulations showed that central vascular compliance variation during a cardiac cycle, peripheral resistance, and cardiac output (CO) collectively contributed to the time delay, amplitude overshoot, and phase shift of PPG responses. Continuous simulations showed that the PPG complexity could be generated by random stimuli, which were subsequently influenced by the autocorrelation patterns of the stimuli. Importantly, the relationship between complexity and hemodynamics as predicted by our model aligned well with the experimental analysis. HFD and ACF had significant contributions to BP, displaying stability even in the presence of high CO fluctuations. In contrast, morphological features exhibited reduced contribution in unstable hemodynamic conditions.Temporal complexity patterns are essential to single-site PPG-based BP estimation.Understanding the physiological implications of these patterns can aid in the development of algorithms with clear interpretability and optimal structures.