Work

Noninvasive continuous blood pressure prediction using FlexNIRS and machine learning during carotid endarterectomy

Year: 2025

Type: article

Abstract: Significance: Continuous blood pressure (BP) monitoring is crucial for maintaining hemodynamic stability and complication prevention. Near-infrared spectroscopy photoplethysmography (NIRS-PPG) offers a noninvasive alternative to arterial lines (A-line) for continuous BP monitoring. Aim: We aim to assess whether a wearable NIRS-PPG device (FlexNIRS) can estimate mean arterial pressure (MAP) using linear and Gaussian process regression (GPR) models. Approach: NIRS-PPG signals were recorded bilaterally in 10 patients undergoing carotid endarterectomy. Subject-specific linear regression and GPR models predicted MAP based on heart rate and peak features of the NIRS-PPG signal. A-line readings served as the reference. Results: , mean absolute error, and root mean squared error were statistically significant. The highest accuracy was observed contralateral to the surgical site using the 2.8-cm source-detector separation. Conclusions: This preliminary study supports the feasibility of noninvasive MAP estimation using NIRS-PPG and machine learning. The approach may provide a practical alternative for BP monitoring after A-line removal in postoperative and intensive care unit settings.

Source: Journal of Biomedical Optics

DOI: 10.1117/1.jbo.30.s2.s23913