Non-invasive continuous blood pressure monitoring method based on GA-MIV-BP neural network model
TAN Xia1, JI Zhong1,2,ZHANG Yadan1
1. College of Biological Engineering, Chongqing University, Chongqing 400044, China
2. Chongqing Medical Electronics Engineering Technology Center, Chongqing 40044, China
Abstract:Aiming at the shortcomings of existing blood pressure measurement models based on pulse wave transit time or pulse wave parameters, a non-invasive continuous blood pressure measurement based on GA-MIV-BP neural network model is proposed. The factors that greatly influence blood pressure from the extracted pulse wave transit time and pulse wave parameters are selected by the mean impact value (MIV) method. These factors are used as inputs, and the actual blood pressure values as outputs, to train the BP neural network model. The individual parameters are then optimized using a genetic algorithm (GA) to establish the GA-MIV-BP neural network model. Bland-Altman consistency analysis indicated that the two values were consistent and interchangeable. Therefore, this algorithm is of great significance to promote the clinical application of a non-invasive continuous blood pressure monitoring method.
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