Blood Pressure Assessment with Differential Pulse Transit Time and Deep Learning: A Proof of Concept

Modern clinical environments are laden with technology devices continuously gathering physiological data from patients. This is especially true in critical care environments, where life-saving decisions may have to be made on the basis of signals from monitoring devices. Hemodynamic monitoring is essential in dialysis, surgery, and in critically ill patients. For the most severe patients, blood pressure is normally assessed through a catheter, which is an invasive procedure that may result in adverse effects. Blood pressure can also be monitored noninvasively through different methods and these data can be used for the continuous assessment of pressure using machine learning methods. Previous studies have found pulse transit time to be related to blood pressure. In this short paper, we propose to study the feasibility of implementing a data-driven model based on restricted Boltzmann machine artificial neural networks, delivering a first proof of concept for the validity and viability of a method for blood pressure prediction based on these models.

For the most severe patients (e.g., dialysis, surgery, and the critically ill), blood pressure is normally assessed through invasive catheters. Alternatively, noninvasive methods have also been developed for its monitorization. Data obtained from noninvasive measurements can be used for the continuous assessment of pressure using machine learning methods. In this study, a restricted Boltzmann machine artificial neural network is used to present a first proof of concept for the validity and viability of a method for blood pressure prediction.