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Open AccessArticle

A Non-Invasive Continuous Blood Pressure Estimation Approach Based on Machine Learning

by Shuo Chen 1, Zhong Ji 1,2,*, Haiyan Wu 1 and Yingchao Xu 1
1
College of Bioengineering, Chongqing University, Chongqing 400044, China
2
Chongqing Medical Electronics Engineering Technology Center, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(11), 2585; https://doi.org/10.3390/s19112585
Received: 28 March 2019 / Revised: 20 May 2019 / Accepted: 31 May 2019 / Published: 6 June 2019
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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. View Full-Text
Keywords: pulse waveform; pulse transit time (PTT); genetic algorithm (GA); machine learning; mean influence value (MIV) pulse waveform; pulse transit time (PTT); genetic algorithm (GA); machine learning; mean influence value (MIV)
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Chen, S.; Ji, Z.; Wu, H.; Xu, Y. A Non-Invasive Continuous Blood Pressure Estimation Approach Based on Machine Learning. Sensors 2019, 19, 2585.

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