Wu Yudong, Zhong Shuncong, Shen Yaochun
Laboratory of Optics, Terahertz and Non-destructive Testing & Evaluation, School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108.
Fujian Key Laboratory of Medical Instrument and Pharmaceutical Technology, Fuzhou, 350108.
Zhongguo Yi Liao Qi Xie Za Zhi. 2017 Jul 30;41(4):235-239. doi: 10.3969/j.issn.1671-7104.2017.04.001.
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 noninvasive blood pressure measurement method based on EEMD (ensemble empirical mode decomposition) and ANN (artificial neural networks) were proposed. In the experiment, a total of 19 500 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.
