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Background: To propose a new method for real-time monitoring of human blood pressure under blood loss (BPBL), this article combines pulse transit time (PTT) and heart rate variability (HRV) as input parameters in order to establish a model for the estimation of BPBL.Methods: Effective parameters such as PTT, R-R internal (RRI), and HRV were extracted and used to establish the blood pressure (BP) estimation. Three BP estimation models were established: the PTT model, the RRI model, and the HRV model, and they were divided into experimental group and control group. Finally, the effects of different estimation models on the accuracy of BPBL estimation were evaluated based on the experimental results. Results:The Pearson correlation coefficients R were 0.7731, 0.8943 and 0.9169 for the PTT model, the RRI model, and the HRV model, respectively. The root means square error of the estimation set (RMSEP) were 16.83 mmHg, 11.87 mmHg and 10.59 mmHg, respectively. Conclusion:The results suggest that the accuracy of the BPBL estimated by the RRI and HRV models is better than that of the PTT model, which means that both RRI and HRV can enhance the accuracy of BPBL estimation, and HRV seems to be more effective in improving the accuracy of BP prediction compared to RRI. These results provide a new idea for other scholars in the field of BPBL estimation research.

Background: To propose a new method for real-time monitoring of human blood pressure under blood loss (BPBL), this article combines pulse transit time (PTT) and heart rate variability (HRV) as input parameters in order to establish a model for the estimation of BPBL.Methods: Effective parameters such as PTT, R-R internal (RRI), and HRV were extracted and used to establish the blood pressure (BP) estimation. Three BP estimation models were established: the PTT model, the RRI model, and the HRV model, and they were divided into experimental group and control group. Finally, the effects of different estimation models on the accuracy of BPBL estimation were evaluated based on the experimental results.Results: The Pearson correlation coefficients R were 0.7731, 0.8943 and 0.9169 for the PTT model, the RRI model, and the HRV model, respectively. The root means square error of the estimation set (RMSEP) were 16.83 mmHg, 11.87 mmHg and 10.59 mmHg, respectively.Conclusion: The results suggest that the accuracy of the BPBL estimated by the RRI and HRV models is better than that of the PTT model, which means that both RRI and HRV can enhance the accuracy of BPBL estimation, and HRV seems to be more effective in improving the accuracy of BP prediction compared to RRI. These results provide a new idea for other scholars in the field of BPBL estimation research.

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