Adaptation to non-invasive continuous blood pressure monitoring

1984 ◽  
pp. 57-63 ◽  
Author(s):  
A. C. Pessina ◽  
P. Palatini ◽  
G. Sperti ◽  
L. Cordone ◽  
E. Ventura ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Monitoring ◽  
Pressure Monitoring ◽  
Non Invasive ◽  
Continuous Blood Pressure

Non-invasive continuous blood pressure monitoring: a review of current applications

2013 ◽  
Vol 7 (1) ◽  
pp. 91-101 ◽  
Author(s):  
Elena Chung ◽  
Guo Chen ◽  
Brenton Alexander ◽  
Maxime Cannesson
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Monitoring ◽  
Pressure Monitoring ◽  
Non Invasive ◽  
Continuous Blood Pressure

scholarly journals Tissue-Informative Mechanism for Wearable Non-invasive Continuous Blood Pressure Monitoring

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Sung Hun Woo ◽  
Yun Young Choi ◽  
Dae Jung Kim ◽  
Franklin Bien ◽  
Jae Joon Kim
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Monitoring ◽  
Pressure Monitoring ◽  
Non Invasive ◽  
Continuous Blood Pressure

scholarly journals Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5543 ◽  
Author(s):  
Haiyan Wu ◽  
Zhong Ji ◽  
Mengze Li
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Bp Neural Network ◽  
Blood Pressure Monitoring ◽  
Individual Characteristics ◽  
Pressure Monitoring ◽  
Estimation Errors ◽  
Non Invasive ◽  
The Impact ◽  
Continuous Blood Pressure

Blood pressure is an extremely important blood hemodynamic parameter. The pulse wave contains abundant blood-pressure information, and the convenience and non-invasivity of its measurement make it ideal for non-invasive continuous monitoring of blood pressure. Based on combined photoplethysmography and electrocardiogram signals, this study aimed to extract the waveform information, introduce individual characteristics, and construct systolic and diastolic blood-pressure (SBP and DBP) estimation models using the back-propagation error (BP) neural network. During the model construction process, the mean impact value method was employed to investigate the impact of each feature on the model output and reduce feature redundancy. Moreover, the multiple population genetic algorithm was applied to optimize the BP neural network and determine the initial weights and threshold of the network. Finally, the models were integrated for further optimization to generate the final individualized continuous blood-pressure monitoring models. The results showed that the predicted values of the model in this study correlated significantly with the measured values of the electronic sphygmomanometer. The estimation errors of the model met the Association for the Advancement of Medical Instrumentation (AAMI) criteria (the SBP error was 2.5909 ± 3.4148 mmHg, and the DBP error was 2.6890 ± 3.3117 mmHg) and the Grade A British Hypertension Society criteria.

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