摘要
血压是衡量人体心血管系统功能的一个重要指标。该文针对电子血压计不能实现血压的无创连续测量等问题,提出一种基于EEMD和ANN算法的无创血压测量方法。实验分析了MIMIC数据库中的19 500个脉搏波信号,通过EEMD对脉搏波进行分解,提取第4层分解信号的10个特征参数作为ANN的输入,脉搏波对应的血压作为ANN的输出进行血压模型的训练,并对模型进行误差分析。实验结果表明,模型的测试误差达到美国医疗器械促进协会(AAMI)制定的标准,通过该方法可实现血压的无创连续测量。
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 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.
出处
《中国医疗器械杂志》
2017年第4期235-239,共5页
Chinese Journal of Medical Instrumentation
基金
国家自然科学基金资助项目(51675103)
教育部高等学校博士学科点科研基金(博导类:20133514110008)
国家卫生和计划生育委员会科研基金(WKJ-FJ-27)
福建省杰出青年基金(滚动资助计划
2014J07007)
关键词
血压
光电容积脉搏波
集合经验模态分解
神经网络
blood pressure, photoplethysmography signal, ensemble empirical mode decomposition, artificial neural networks
