Method study of non-invasive continuous blood pressure prediction based on pulse wave and electrocardiosignal
Objective To study the non-invasive continuous blood pressure prediction method based on pulse wave and electrocardiosignal.Methods A total of 300 cases were selected from MIMIC-Ⅲ database to build blood prediction model and model verification.Meanwhile,121 cases were collected which were hospitalized in the Intensive Care Unit of Fujian Provincial Hospital from January to June 2022 for test model.The arterial blood pressure,photoplethysmography,and electrocardiography signal of patients were collected.Two blood pressure prediction models were built.The first one was artificial feature parameter model that was built based on eight artificially collected feature parameters.One was feature fusion model that was fused and built based on the eight feature parameters and the other one feature collected from convolutional neural network.These two prediction models were verified and tested.The evaluation indexes applied mean absolute error(MAE),standard deviation(SD),and root mean square error(RMSE).Evaluation was proceeded according to the internationally recognized specified standard of(Association for the Advancement of Medical Instrumentation,AAMI)to compare the predictive ability of both models.Results MIMIC-Ⅲ data were applied to evaluate both models.The MAE and SD of feature fusion model were consistent with the standard of AAMI.RMSE was lower than it of artificial feature parameter model.The actual collected data of critical patients were applied to evaluate.The SD of systolic pressure,MAE,and SD of diastolic pressure of feature fusion model met the standard of AAMI.RMSE was also lower than that of artificial feature parameters.Conclusion The predictive ability of feature fusion model is better than artificial feature parameter models.
万方数据
维普
