Name
Abstract | ||
|---|---|---|
Blood pressure 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3127404.3127413 | 12TH CHINESE CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING (CHINESECSCW 2017) |
Keywords | Field | DocType |
Blood pressure monitoring, Photoplethysmogram, machine learning, clustering, gradient boosting | Nonlinear system,Blood pressure monitoring,Computer science,Pulse (signal processing),Human–computer interaction,Blood pressure,Artificial intelligence,Cluster analysis,Pattern recognition,Photoplethysmogram,Speech recognition,Korotkoff sounds,Gradient boosting | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiqiang Zhang | 1 | 13 | 6.37 |
| yu miao | 2 | 4 | 7.18 |
| Lifang Meng | 3 | 0 | 1.01 |
| Xiaoqin Xie | 4 | 2 | 1.74 |
| Haiwei Pan | 5 | 52 | 21.31 |
| Xiaoning Feng | 6 | 2 | 1.37 |