Estimation of Blood Pressure from Arterial Blood Pressure using PPG Signals

Abstract

Predicting Blood pressure from Photoplethysmography (PPG) signals is an active area of research and there have been many studies exploring the feasibility of this approach. This paper elaborates on a technique for the estimation of continuous Arterial blood pressure (ABP) waveform using PPG signals as inputs in a developed deep-learning model. The ultimate goal is estimating the Blood pressure, but unlike the standard regression models for predicting Blood pressure by systolic BP (SBP) and Diastolic BP (DBP), this approach calculates SBP and DBP from the estimated ABP waveform, which enables further analysis to enhance the BP estimation. The best-obtained results are an MAE of 8.40mmHg, and an MAE of 11.1mmHg and 7mmHg for SBP and DBP respectively. The promising prediction of SBP and DBP using our proposed machine learning model has the potential to improve clinical decision-making and resource allocation process in emergency situations.