Estimation Model for Blood Pressure Based on Clustering and Gradient Boosting
Authors: 
Zhiqiang Zhang, Yu Miao, Lifang Meng, Xiaoqin Xie, Haiwei Pan, Xiaoning FengAuthors Info & Claims
Pages 48 - 55
Abstract
Blood pressure1 is one of the important physiological signals of human body. How to measure blood pressure effectively is of great significance in medical treatment and daily life. The traditional method of blood pressure measurement is most based on Korotkoff sound, which need to put pressure on individuals, operate tediously, can not monitor continuously, and is easy to cause discomfort to the individuals, so it is necessary to seek a better method for continuous noninvasive blood pressure monitoring. Thanks to the development of sensor technology, people can easily obtain Photoplethysmogram (PPG) signals of human pulse, and many studies have also made estimation of blood pressure based on PPG signals. One kind of method can indirectly obtain pulse transit time using PPG signal, and then inferred the blood pressure, but there is also a problem of complex operation; another class of method extracted useful features from the PPG signal, and then built a model on features to estimate the blood pressure. On this basis, this paper built linear and nonlinear estimation model on PPG signals and blood pressure, based on the method of machine learning, and then improved the model by combining with clustering and gradient boosting techniques. The experimental results show that this model can effectively improve the effect of blood pressure estimation.
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Index Terms
- Estimation Model for Blood Pressure Based on Clustering and Gradient Boosting
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Published In
September 2017
269 pages
ISBN:9781450353526
DOI:10.1145/3127404
- General Chairs:
- Zili Zhang,
- Ning Gu,
- Program Chairs:
- Shaozi Li,
- Tun Lu,
- Li Li
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Published: 22 September 2017
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ChineseCSCW '17
ChineseCSCW '17: Chinese Conference on Computer Supported Cooperative Work and Social Computing
September 22 - 23, 2017
Chongqing, China
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ChineseCSCW '17 Paper Acceptance Rate 21 of 84 submissions, 25%;
Overall Acceptance Rate 21 of 84 submissions, 25%
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