[1]周慧婕,陈小惠*,于舒洋,等.基于 PSO-ELM 的无创血压检测方法[J].计算机技术与发展,2022,32(12):63-68.[doi:10. 3969 / j. issn. 1673-629X. 2022. 12. 010]
　ZHOU Hui-jie,CHEN Xiao-hui*,YU Shu-yang,et al.Non-invasive Blood Pressure Detection Method Based on PSO-ELM[J].,2022,32(12):63-68.[doi:10. 3969 / j. issn. 1673-629X. 2022. 12. 010]
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基于 PSO-ELM 的无创血压检测方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年12期
页码:
63-68
栏目:
媒体计算
出版日期:
2022-12-10

文章信息/Info

Title:
Non-invasive Blood Pressure Detection Method Based on PSO-ELM
文章编号:
1673-629X(2022)12-0063-06
作者:
周慧婕; 陈小惠* ; 于舒洋; 张桂珍; 张芳敬
南京邮电大学 自动化学院、人工智能学院,江苏 南京 210023
Author(s):
ZHOU Hui-jie; CHEN Xiao-hui* ; YU Shu-yang; ZHANG Gui-zhen; ZHANG Fang-jing
School of Automation / Artificial Intelligence,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
关键词:
脉搏波传递时间; 粒子群优化; 极限学习机; 人体特征; 血压检测
Keywords:
pulse wave transit time ( PWTT) ; particle swarm optimization ( PSO) ; extreme learning machine ( ELM) ; human body char鄄acteristics; blood pressure detection
分类号:
TP274
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 12. 010
摘要:
为降低人体特征差异对血压预测模型的影响,进一步提高血压预测的准确度,该文提出一种基于脉搏波传递时间改进的无创血压检测方法。 首先由采集到的光电容积　脉搏波信号( PPG) 与心电信号( ECG) 求得的脉搏波传递时间(PWTT) 以及体重( weight) ,计算出血压粗略值;然后结合人体固有的生理参数作为 PSO-ELM(Particle Swarm Optimization-Extreme Learning Machine) 预测模型的输入参数,从而获得最终的血压预测值。 通过与 SVM、RF 和传统水银计测量方法对比发现,PSO-ELM 　方法求得的舒张压( DBP) 与收缩压( SBP) 平均绝对误差( MAE) 均满足美国医疗仪器促进协会( AAMI)制定的依5 mmHg 的标准,与水银计测血压方法具有更好的一致性,并且在依5 mmHg 误差范围的命中率均高于 SVM 与 RF方法的命率。
Abstract:
In order to reduce the impact of differences in human body characteristics on blood pressure prediction models and furtherimprove the accuracy of blood 　　pressure prediction,we propose a new type of non - invasive blood pressure detection method based onParticle Swarm Optimization - Extreme Learning 　　Machine ( PSO-ELM) . First,the rough value of blood pressure is calculated by thepulse wave transit time ( PWTT) and weight ( weight) obtained from the 　collected photoplethysmography signal ( PPG) and electrocardiographic signal ( ECG) . Then the inherent physiological parameters of the human body is 　combined as PSO-ELM to predict the inputparameters of the model for the final blood pressure prediction value. By comparing SVM,RF and traditional mercury meter measurementmethod,the average absolute error ( MAE) of diastolic blood pressure ( DBP) and systolic blood pressure ( SBP) obtained by PSO -ELM 　method meets the requirements of the American Association for the Advancement of Medical Instruments ( AAMI) . The standardof 依5 mmHg has better consistency with the method of measuring blood pressure with mercury meter,and the hit rate within the errorrange of 依5 mmHg is higher than that of SVM and 　RF methods.

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更新日期/Last Update: 2022-12-10