一种结合传统示波法与新型<em>k</em>-means算法的血压判定方法

doi:10.3969/j.issn.0258-8021.2020.02.04

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Abstract The improvement of the accuracy of measuring blood pressure is an important issue to prevent hypertension. Oscillography uses statistical experience to determine a set of coefficients, which is inaccurate enough in the measurement of blood pressure. In this work, a method of measuring blood pressure by combining multiple techniques was proposed. Five characteristic parameters from the de-interference oscillometric waveform envelope were extracted to match the envelope to the best set of coefficients through the classification model and measure the blood pressure by oscillography. The classification model was trained offline. Specifically, the first thing was to uniquely cluster the envelopes using the novel k-means. Next, adaptively determined a set of optimal coefficients for each type of envelope according to the gradient descent, and there was a one-to-one correspondence between the centroid vectors and the calculated coefficients. Based on above mentioned, the classification model was finally constructed. There were 33 subjects in the experiment. Experimental results showed that the proposed blood pressure measurement was similar to that of mercury method, the correlation coefficient could reach 0.91, and met the AAMI-SP10 standards even the first-level requirements of ESH. Furthermore, the proposed measurement was superior to oscillography, with regard to the deviations between the estimated blood pressure and the target blood pressure, the number of deviations within 5 mmHg was increased by 10.5% on average, and the number of deviations within 10 mmHg was increased by 6.8% on average.

Key words： blood pressure traditional oscillography novel k-means algorithm gradient descent

Received: 19 September 2018

PACS:

R318

Corresponding Authors: ^*E-mail: lqp039@163.com

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	Hua Bin
	Chen Yuting
	Lv Qingpu

Cite this article:

Hua Bin,Chen Yuting,Lv Qingpu. A Method of Measuring Blood Pressure by Combining with Traditional Oscillography and Novel k-Means Algorithm[J]. Chinese Journal of Biomedical Engineering, 2020, 39(2): 152-159.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.02.04 OR http://cjbme.csbme.org/EN/Y2020/V39/I2/152

[1] 蒋巍巍, 季忠. 无创血压测量方法的研究进展[J]. 中华高血压杂志,2015(7): 685-689.
[2] James PA, Oparil S, Carter BL, et al. 2014 evidence-based guideline for the management of high blood pressure in adults: Report from the panel members appointed to the Eighth Joint National Committee (JNC 8)[J]. JAMA, 2014, 311(5): 507-520.
[3] O′ Brien E, Waeber B, Parati G, et al. Blood pressure measuring devices: Recommendations of the European Society of Hypertension.[J]. British Medical Journal, 2001, 322(7285):531-536.
[4] 霍春宝,李玲姣,张亮. 基于ARM的动态血压测量系统[J]. 电子测量技术,2008(3):91-93.
[5] 全晓莉, 何为, 张维维. 利用示波法原理测量血压的一种新方法[J]. 航天医学与医学工程,2006,19(1):71-73.
[6] Benmira AM, Perez-Martin A, Coudray S, et al. Systolic peak foot-to-apex time interval: A novel oscillometric technique for systolic blood pressure measurement [J]. Journal of Hypertension, 2017, 35(5): 1002-1010.
[7] 谢寒霜,王瑞平,王艳洁,等. 基于脉搏波特征参数的无创连续血压监测[J]. 中国医疗设备,2017,32(10):39-43.
[8] 吴育东,钟舜聪,沈耀春. 基于SWT和ANN的无创连续血压测量方法研究[J]. 中国医疗设备,2017,32(5):22-27.
[9] 赵谞博,赫英迪,李信政,等. 基于支持向量回归的人体血压预测方法[J]. 燕山大学学报,2017,41(5):438-443.
[10] 侯功,邓辉. 基于示波法的血压测量新方法[J]. 现代电子技术,2012,35(22):147-150.
[11] 王维维, 蒲宝明, 李生金,等. 基于示波法测量血压的算法改进[J]. 计算机系统应用,2012,21(2):196-199.
[12] 邓静,王鹏飞,査富生,等. 柯氏音法与示波法相结合的新型血压测量仪研究[J]. 北京生物医学工程,2011,30(2):173-177,209.
[13] 许怀湘, 房兴业. 采用示波原理间接测量血压方法的进展[J]. 航天医学与医学工程,2000,13(3): 231-234.
[14] Baker PD, Westenskow DR, Kück K. Theoretical analysis of non-invasive oscillometric maximum amplitude algorithm for estimating mean blood pressure [J]. Medical & Biological Engineering & Computing, 1997, 35(3):271-278.
[15] 张冬雪, 苑津莎, 李中. 一种改进阈值法小波去噪的信号包络分析方法研究[J]. 电力科学与工程,2010,26(6):6-10.
[16] 龙韬臣,巨建树,李德玉,等. 示波法血压测量技术研究进展[J]. 中国医疗器械杂志,2018,42(1):41-45.
[17] Wolberg G, Alfy I. An energy-minimization framework for monotonic cubic spline interpolation[J]. Journal of Computational and Applied Mathematics, 2002, 143(2): 145-188.
[18] 王希星, 洪葵, 苏海. 示波法电子血压计:临床医生应当了解的知识[J]. 中华高血压杂志,2016(9):827-830.
[19] 李雪情,张永亮,郑莹莹,等. 基于示波法和高斯拟合的血压测量方法[J]. 传感技术学报,2010,23(12):1679-1685.
[20] 吕海姣,严壮志,陆维嘉. 一种基于脉搏波的无创连续血压测量方法[J]. 中国医疗器械杂志,2011,35(3):169-173.
[21] 苏志刚, 朱海玲, 郝敬堂. 基于高斯混合模型的脉搏波特征提取方法[J]. 中国医学物理学杂志,2018,35(1):76-82.
[22] 王陈海,张鲁闽,马继民,等. 基于奇异值矩阵分解的腕式示波法血压测量算法[J]. 生物医学工程学杂志,2011,28(4):715-720.
[23] 瞿诗华, 吴华炜, 钱志余, 等. 基于脉搏波特征参数的无创血压测量改进算法[J]. 生物医学工程研究,2018(1): 36-41.
[24] 张玮奇. 基于神经网络提高准确度的血压测量系统研究[J]. 中国医疗器械杂志,2011,35(4):246-249.
[25] 温亮,李振波,陈佳品,等. 基于高斯拟合的神经网络血压测量算法[J]. 传感器与微系统,2014,33(4):132-134.
[26] 周世兵, 徐振源, 唐旭清. 新的K-均值算法最佳聚类数确定方法[J]. 计算机工程与应用,2010,46(16):27-31.
[27] Arora JS. Jan A. Snyman, Practical Mathematical Optimization: An introduction to basic optimization theory and classical and new gradient-based algorithms[J]. Structural & Multidisciplinary Optimization, 2006, 31(6):249-249.
[28] Lewis J E, Boyle E, Magharious L, et al. Evaluation of a community-based automated blood pressure measuring device[J]. Canadian Medical Association Journal, 2002, 166(9):1145-1148.
[29] 俞黎曦,王杨,李卫,等. ESH推荐的血压仪评价标准的统计学验证[J]. 首都医药,2009,16(20):8-9.
[30] 向海燕, 俞梦孙, 刘娟. 用有创血压验证示波法测量血压的准确性[J]. 北京生物医学工程, 2005, 24(3): 195-198.