Author Login

Correspondence to Author: Ripoll Vicent Ribas, 

Eurecat, Centre Tecnològic de Catalunya, eHealth Unit, Barcelona, Spain.

ABSTRACT: 
Background : Many technological instruments are used in modern therapeutic settings to continuously collect patients’ physiological data. This is particularly true in critical care settings, where signals from monitoring equipment may need to be considered when making potentially lifesaving decisions. Hemodynamic monitoring is crucial for critically ill patients, dialysis patients, and surgical patients. Blood pressure is often measured for the most seriously ill individuals using a catheter, which is an intrusive process with potential side effects. Additionally, blood pressure can be continuously measured utilizing machine learning techniques by employing a variety of noninvasive monitoring techniques. Previous research has discovered a relationship between blood pressure and pulse transit time. In this little essay, In order to provide a first proof of concept for the validity and viability of a method for blood pressure prediction based on constrained Boltzmann machine artificial neural networks, we propose to investigate the viability of developing a data-driven model.
Synopsis and Main Takeaways: By using invasive catheters, blood pressure is typically measured for the sickest patients (dialysis, surgery, critically unwell). As an alternative, non-invasive techniques for its monitoring have also been developed. Machine learning techniques can be used to continuously assess pressure using data from noninvasive measures. In this paper, a first proof of concept for the validity and feasibility of a blood pressure prediction approach is shown using a constrained Boltzmann machine artificial neural network.

Citation:

Ripoll Vicent Ribas. A Proof of Concept for Blood Pressure Monitoring with Differential Pulse Transit Time and Deep Learning. The American Journal of Kidney Diseases 2024.