ComputingComputer ScienceHealth InformaticsBiomedical Signal ProcessingMachine LearningPhysiological Systems ModelingBlood Pressure ControlBlood Pressure ManagementBlood Pressure Signal AnalysisCardiovascular MonitoringHemodynamics ModelingHypertension ManagementAccuracyBlood PressureBlood Pressure EstimationDiastolic Blood Pressure EstimationFeature Extraction From Blood Pressure SignalsMachine Learning For Blood Pressure EstimationPulse Transit Time AnalysisSystolic Blood Pressure Estimation
Considering the existing issues of traditional blood pressure (BP) measurement methods and non-invasive continuous BP measurement techniques, this study aims to establish the systolic BP and diastolic BP estimation models based on machine learning using pulse transit time and characteristics of pulse waveform. In the process of model construction, the mean impact value method was introduced to investigate the impact of each feature on the models and the genetic algorithm was introduced to implement parameter optimization. The experimental results showed that the proposed models could effectively describe the nonlinear relationship between the features and BP and had higher accuracy than the traditional methods with the error of 3.27 ± 5.52 mmHg for systolic BP and 1.16 ± 1.97 mmHg for diastolic BP. Moreover, the estimation errors met the requirements of the Advancement of Medical Instrumentation and British Hypertension Society criteria. In conclusion, this study was helpful in promoting the practical application of methods for non-invasive continuous BP estimation models.
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Chih-Chung Chang, Chih‐Jen Lin · ACM Transactions on Intelligent Systems and Technology · 2011 · 41.3K citations
MIMIC-III, a freely accessible critical care database
Alistair E. W. Johnson, Tom Pollard, Lu Shen et al. · Scientific Data · 2016 · 8.1K citations · Full text