Noninvasive Continuous Blood Pressure Measurement Method Based on EEMD and ANN

VernacularTitle:血压测量的EEMD和ANN的方法研究
Author: Yudong WU ¹ ; Shuncong ZHONG ; Yaochun SHEN
Author Information

1. 福州大学机械工程及自动化学院光学/太赫兹及无损检测实验室
Keywords: blood pressure; photoplethysmography signal; ensemble empirical mode decomposition; artificial neural networks
From: Chinese Journal of Medical Instrumentation 2017;41(4):235-239
CountryChina
Language:Chinese
Abstract: Blood pressure is an important index to measure the function of human cardiovascular system. In order to solve the problem of non-invasive continuous measurement of blood pressure in electronic sphygmomanometer, a non-invasive blood pressure measurement method based on EEMD (ensemble empirical mode decomposition) and ANN (artificial neural networks) were proposed. In the experiment, a total of 19500 pulse wave signals from THE MIMIC DATABASE were analyzed and subsequently the pulse wave was decomposed by EEMD. Furthermore, 10 characteristic parameters of the 4th layer decomposition signal were extracted as the input of ANN. The blood pressure corresponding to the pulse wave was taken as the output of ANN to train the BP (blood pressure) model. The error analysis of the model was carried out. The results indicated that the error of the model meets the standards of the American Association for the advancement of medical instrumentation (AAMI). Therefore, this method can be employed in noninvasive continuous measurement of blood pressure.