Using near-infrared spectroscopy and a random forest regressor to estimate intracranial pressure

Abstract

SignificanceIntracranial pressure (ICP) measurements are important for patient treatment but are invasive and prone to complications. Noninvasive ICP monitoring methods exist, but they suffer from poor accuracy, lack of generalizability, or high cost.AimWe previously showed that cerebral blood flow (CBF) cardiac waveforms measured with diffuse correlation spectroscopy can be used for noninvasive ICP monitoring. Here we extend the approach to cardiac waveforms measured with near-infrared spectroscopy (NIRS).ApproachChanges in hemoglobin concentrations were measured in eight nonhuman primates, in addition to invasive ICP, arterial blood pressure, and CBF changes. Features of average cardiac waveforms in hemoglobin and CBF signals were used to train a random forest (RF) regressor.ResultsThe RF regressor achieves a cross-validated ICP estimation of 0.937r2, 2.703-mmHg2 mean squared error (MSE), and 95% confidence interval (CI) of [ − 3.064 3.160 ] mmHg on oxyhemoglobin concentration changes; 0.946r2, 2.301-mmHg2 MSE, and 95% CI of [ − 2.841 2.866 ] mmHg on total hemoglobin concentration changes; and 0.963r2, 1.688 mmHg2 MSE, and 95% CI of [ − 2.450 2.397 ] mmHg on CBF changes.ConclusionsThis study provides a proof of concept for the use of NIRS in noninvasive ICP estimation.