Non-Interchangeability between Heart Rate Variability and Pulse Rate Variability During Supine-to-Stand Tests

Heart rate variability (HRV) is widely recognized as a valuable biomarker for assessing autonomic cardiac regulation. Pulse rate variability (PRV) is a common surrogate of HRV given the wide usability of PPG in commercially available devices. However, there is no clear conclusion on whether PRV can replace HRV given their different physiological mechanisms. This study evaluates the interchangeability of young adults HRV and PRV during supine-to-stand (STS) tests which are known as common posture transitions in daily life monitoring. Fifteen features from time, frequency and nonlinear domains were extracted from both electrocardiography and PPG signals. Paired t-tests and Wilcoxon signed-rank tests examined the difference between the extracted HRV and PRV features during supine, transition and standing phases separately. One feature showed significant difference in the supine phase, and this discrepancy increased to four in the transition and standing phases. These findings suggested that PRV is different from HRV in the STS tests, despite the fact that both metrics can reflect the sympathetic activation triggered by the posture changes.

A Mathematical Model for Skin Sympathetic Nerve Activity Simulation

Autonomic nervous system is important for cardiac function regulation. Modeling of autonomic cardiac regulation can contribute to health tracking and disease management. This study proposed a mathematical model that simulates autonomic cardiac regulation response to Valsalva Maneuver, which is a commonly used test that provokes the autonomic nervous system. Dataset containing skin sympathetic nervous activity extracted from healthy participants' ECG was used to validate the model. In the data collection procedure, each participant was required to perform Valsalva Maneuver. The preliminary result of modeling for one subject is presented, and the model validation result showed that the root measure square error between the simulated and measured average skin sympathetic nervous activity is 0.01mV. The model is expected to be further developed, evaluated using the dataset including 41 subjects, and ultimately applied for capturing the early signs of cardiac dysfunction in the future.