A novel approach to estimate blood pressure of blood loss continuously based on stacked auto-encoder neural networks

Abstract

ObjectiveA novel blood pressure of blood loss (BPBL) estimation method with multi-parameter fusion based on stacked auto-encoder neural networks (SAE) is proposed in this work that aims to realize non-invasive continuous monitoring of BPBL. MethodsOur approach combined PTT, R-peak to R-peak interval (RRI), peak-foot values (PFV) and peak values (PV), extracted from electrocardiogram (ECG) and photoplethysmo- gram (PPG) for the estimation of BPBL. We used these parameters to establish a PPG-PTT and an RRI-PTT model, then employed the SAE method to get the calculation model between systolic blood pressure (SBP) diastolicblood pressure (DBP) and the characteristic parameters. ResultsThe animal experimental results based on five pigs demonstrated that the RRI-PTT model estimated the BPBL more accuratly and less error compared with the PPG-PTT model (the correlation between estimated SBP & DBP and actual SBP & DBP were 0.9954 and 0.9963, and the root mean square error for SBP & DBP were 2.56 and 2.57 mmHg). ConclusionThe PFV, PV, and RRI extracted in this work were correlated to BPBL, which can enhance the accuracy of BPBL estimation. In addition, the experimental results showed that the SAE method played a pivotal role in the non-invasive estimation of BPBL. SignificanceThe estimation method proposed in this study can innovate and expand the research work of non-invasive BP and BPBL, and provide a feasible practice for the non-invasive prediction of BPBL in the future.