Heart rate variability enhances the accuracy of non-invasive continuous blood pressure estimation under blood loss

To propose a new method for real-time monitoring of blood pressure of blood loss (BPBL), this article combines pulse transit time (PTT) and heart rate variability (HRV) as input parameters to build a model for BPBL estimation. In this article, effective parameters such as PTT, R-R interval (RRI), and HRV were extracted and used to establish the blood pressure (BP) estimation. Three BP estimation models were created: the PTT model, the RRI model, and the HRV model, and they were divided into an experimental group and a control group. Finally, the effects of the different estimation models on the accuracy of BPBL were evaluated using the experimental results. The result showed that both the RRI model and the HRV model have a good improvement effect on the prediction accuracy of BPBL, and the HRV model has the highest prediction accuracy than the PTT model and the RRI model. The correlation coefficients between the actual systolic BP (SBP) and diastolic BP (DBP) and the estimated SBP and DBP of the HRV model were 0.9580 and 0.9749, respectively, and the root-mean-square error of the HRV model for both SBP and DBP were 7.59 and 6.56 mmHg, respectively. The results suggest that the accuracy of the BPBL estimated by the HRV models is better than that of the PTT model, which means that HRV seems to be more effective in improving the accuracy of BP estimation compared with RRI. These results in this article provide a new idea for other researchers in the field of BPBL estimation research.