An electrical pulse artifact signal for estimating arterial blood pressure: a proof-of-concept study

Objective.Hypertension is a leading cause of mortality worldwide, for which myriad treatment options are available. It is widely considered that continuous measurement of arterial blood pressure (BP) could improve the treatment of hypertension; however, chronically monitoring patient BP remains a significant challenge. In this study, we investigated a novel approach that uses an implantable electrode to generate an artifact signal for predicting arterial BP.Approach.In isoflurane anesthetized rats (n= 10, male), the right common carotid artery was instrumented with a multi-contact cuff electrode to acquire the artifact signal-termed the electro-vascular-gram (EVG) and the contralateral common carotid artery was catheterized to measure intra-arterial BP. The EVG signals were processed (e.g. extract Catch22 features) and applied to linear regression, random forest (RF) regressor, and convolutional neural network models to predict systolic and diastolic BP.Main results.Among the various models tested with the EVG data, the RF model + Catch22 features method achieved the highest performance, yielding predicted BP values (error < 5 mmHg) in 82.6%-100% and 84.1%-99.9% of the testing set for systolic and diastolic, respectively. A 5-fold cross-validation demonstrated similar performance by predicting BP values (error < 5 mmHg) in 91.5 ± 0.1% and 92.4 ± 0.1% of testing data for systolic and diastolic, respectively.Significance.This proof-of-concept study supports the feasibility of using an implantable electrode and machine learning models for potentially measuring arterial BP in continuous fashion. Further system development is warranted prior to clinical translation.