Jugular venous pulse analysis: software and statistical assessment in healthy patients using a non-invasive plethysmography system

Jugular venous pulse (JVP) is a signal closely related to the electrocardiogram (ECG) and returns information not only on cardiac function but also on blood outflow on the brain–heart axis. The JVP is strongly associated with central venous pressure and in medicine it can be assessed through a physical qualitative examination or through ultrasound investigation, which is an operator-dependent technique. From the perspective of non-invasive methodologies to monitor blood pulse, wearable devices usually detect values related to the arterial pulse, not the venous pulse. In scientific literature, there are many useful algorithms for extracting arterial pulse information from wearables, but the same is not true for the venous pulse. In this work, an algorithm is developed and tested for the automatic extraction of the characteristics of JVP and ECG signals. A graphical user interface returns the variables of interest, defined as the time differences between the peaks of the JVP signal ( xa , vx , yx ) and between the peaks of both JVP and ECG signals ( cR, aP, xP, vT, Ra, Tc, xT, Py – 1 ) for each detected heartbeat. A statistical analysis to test the null hypothesis of a constant value in healthy patients among the obtained results showed that linear dependence was unlikely or very weak, while the hypothesis of a constant trend was confirmed with a confidence level of p -value < 0.005. By characterizing the JVP in healthy young adults, this method provides a baseline for its validation in broader populations and potential clinical use.