一种基于多参数融合的无袖带式连续血压测量方法的研究

徐志红; 方震; 陈贤祥; 覃力; 杜利东; 赵湛; 刘杰昕

doi:10.11999/JEIT170238

一种基于多参数融合的无袖带式连续血压测量方法的研究

doi: 10.11999/JEIT170238

2.
(中国科学院电子学研究所北京 100190)
3.
(北京天坛医院北京 100050)

基金项目:

国家自然科学基金(61302033)，北京市自然科学基金(Z16003)，国家重点研发计划(2016YFC1304302)

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出版历程
- 收稿日期: 2017-03-24
- 修回日期: 2017-11-27
- 刊出日期: 2018-02-19

Research About Cuff-less Continuous Blood Pressure Estimation by Multi-parameter Fusion Method

2.
(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
3.
(TianTan Hospital, Beijing 100050, China)

Funds:

The National Natural Science Foundation of China (61302033), The Key Project of Beijing Municipal Natural Science Foundation (Z16003), The National Key Research and Development Project (2016YFC1304302)

摘要

摘要: 针对现有基于脉搏波传输时间的无创连续性血压测量算法精度不高的问题，该文综合考虑心电信号和血氧容积波与血压变化的相关性，提出一种基于BP神经网络的无创连续性血压测量方法。该文首先利用改进的心电信号算法提取出心电信号的R点，利用差分、阈值的方法提取出血氧容积波的特征参数，再经过特征解析，提取出与血压相关的15维特征向量，构建基于BP神经网络的血压计算模型，计算出逐拍的血压值。该方法在天坛医院等单位进行了医学临床比对测试，并通过因子分析法分析了15个特征参数的权重比。实验证明：在预测血压上，脉搏波传输时间的权重，大于相邻特征点之间的时间信息权重，大于脉搏波面积信息权重，大于脉搏波幅值信息权重；该方法精度优于其它相近方法，单次测量的舒张压和收缩压误差的平均值标准差分别是-1.576.12 mmHg和-0.624.82 mmHg，重复测量误差的平均值标准差分别是-2.125.10 mmHg和-2.524.41 mmHg。收缩压和舒张压的测量精度均达到了BHS血压标准的Grade A类和AAMI标准。
- 可穿戴式技术 /
- 连续血压 /
- 多参数融合 /
- 神经网络
Abstract: For the problem of noninvasive continuous blood pressure algorithm with un-accuracy, a novel multi- parameter fusion algorithm based on BP neural network is proposed, according to the formation from electrocardiogram and photoplethysmograph of arterial blood pressure. The improved Pan Tompkins algorithm is used to extract the R peak of electrocardiogram, and difference-threshold algorithm is used to extract the features points of photo-plethysmograph, and the fifteen feature parameters relative to blood pressure are extracted and used to establish the model of blood pressure to estimate the beat-to-beat systolic blood pressure and diastolic blood pressure. The factor analysis method is used to analyze the weight of each parameter. The results show that the weight order is pulse transit time, time information, photoplethysmography area information, amplitude information and area ratio. The algorithm is tested in the TianTan Hospital, and the meansstandard difference of single measurement errors are respectively -1.576.12 mmHg and -0.624.82 mmHg, the means standard difference, D. of repeated measurement errors are respectively -2.125.10 mmHg and -2.524.41 mmHg, for systolic blood pressure and diastolic blood pressure. And the measurement accuracy for systolic blood pressure and diastolic blood pressure reaches Grade A of BHS standard and AAMI standard.
- Wearable technology /
- Continuous blood pressure /
- Multi-parameter fusion /
- Neural network

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参考文献(48)

POON C C and ZHANG Y T. Cuff-less and noninvasive measurements of arterial blood pressure by pulse transit time[C]. International Conference of the Engineering in Medicine Biology Society, Shanghai, China, 2005: 5877-5880.

BUXI D, REDOUTE J M, and YUCE M R. Blood pressure estimation using pulse transit time from bioimpedance and continuous wave radar[J]. IEEE Transactions on Biomedical Engineering, 2017, 64(4): 917-927.

RIBAS V. Continuous blood pressure assessment from a photoplethysmographic signal with Deep Belief Networks[J]. Faseb Journal, 2014, 28(1): Supplement LB674.

PAN J and TOMPKINS W J. A real-time QRS detection algorithm[J]. IEEE Transactions on Biomedical Engineering, 1985, 32(3): 230-236. doi: 10.1109/TBME.1985.325532.

丁有得. 基于容积脉搏波血流多参数测量的研究[D]. [博士论文], 南方医科大学, 2010.

DING Youde. Study on blood flow multi-parameters detecting based on the volume pulse wave[D]. [Ph.D. dissertation], Southern Medical University, 2010.

吴秋玲, 吴蒙. 基于小波变换的语音信息隐藏新方法[J]. 电子与信息学报, 2016, 38(4): 834-840. doi: 10.11999/JEIT150856.

WU Qiuling and WU Meng, One new audio information hidden method based on wavelet transform[J]. Journal of Electronics Information Technology, 2016, 38(4): 834-840. doi: 10.11999/JEIT150856.

吴光文, 王昌明, 包建东, 等. 基于自适应阈值函数的小波阈值去噪方法[J]. 电子与信息学报, 2014, 36(6): 1340-1347. doi: 10.3724/SP.J.1146.2013.00798.

WU Guangwen, WANG Changming, BAO Jiandong, et al. A wavelet threshold de-noising algorithm based on adaptive threshold function[J]. Journal of Electronics Information Technology, 2014, 36(6): 1340-1347. doi: 10.3724/SP.J.1146. 2013.00798.

田晶晶, 李广军, 李强. 一种基于迭代短卷积算法的低复杂度并行FIR滤波器结构[J]. 电子与信息学报, 2014, 36(5): 1151-1157. doi: 10.3724/SP.J.1146.2013.00976.

TIAN Jingjing, LI Guangjun, and LI Qiang. Hardware- efficient parallel structures for linear-phase FIR digital filter based on iterated short convolution algorithm[J]. Journal of Electronics Information Technology, 2014, 36(5): 1151-1157. doi: 10.3724/SP.J.1146.2013.00976.

黄聪, 刘寅. 基于多普勒频偏估计的单帧图像低速运动目标检测方法[J]. 电子与信息学报, 2016, 38(7): 1638-1644. doi: 10.11999/JEIT151078.

HUANG Cong and LIU Yin, Low-speed moving target detection of single frame image based on Doppler shift estimation[J]. Journal of Electronics Information Technology, 2016, 38(7): 1638-1644. doi: 10.11999/ JEIT151078.

黄晓霞, 罗胜钦, 陆明达. 人工神经网络实现稳定的自适应IIR滤波器[J]. 电子与信息学报, 1997, 19(4): 445-450.

HUANG Xiaoxia, LUO Shengqin, and LU Mingda. A circuit of artificial neural network for implementing stable adaptive IIR filter[J]. Journal of Electronics Information Technology, 1997, 19(4): 445-450.

许华健, 杨志伟, 廖桂生, 等. 一种稳健的非均匀杂波协方差矩阵估计方法[J]. 电子与信息学报, 2017, 39(5): 1036-1043. doi: 10.11999/JEIT160747.

XU Huajian, YANG Zhiwei, LIAO Guisheng, et al. Robust approach for clutter covariance matrix estimation with STAP in heterogeneous environment[J]. Journal of Electronics Information Technology, 2017, 39(5): 1036-1043. doi: 10.11999/JEIT160747.

韩庆阳, 王晓东, 李丙玉, 等. EEMD在同时消除脉搏血氧检测中脉搏波信号高频噪声和基线漂移中的应用[J]. 电子与信息学报, 2015, 37(6): 1384-1388. doi: 10.11999/JEIT141390.

HAN Qingyang, WANG Xiaodong, LI Bingyu, et al. Using EEMD to eliminate high frequency noise and baseline drift in pulse blood-oximetry measurement simultaneously[J]. Journal of Electronics Information Technology, 2015, 37(6): 1384-1388. doi: 10.11999/JEIT141390.

唐洪荣, 沈民奋, 李斌. 周期紧支撑径向基函数对BEMD的优化[J]. 电子与信息学报, 2008, 30(1): 149-153. doi: 10.3724/ SP.J.1146.2006.00849.

TANG Hongrong, SHEN Minfen, and LI bin. The improvement of the BEMD using compactly supported RBF[J]. Journal of Electronics Information Technology, 2008, 30(1): 149-153. doi: 10.3724/SP.J.1146.2006.00849.

YUAN Zhaokai, HUANG Xianping, FAN Fuyuan, et al. Analysis of photoplethysmogram on different positions of 253 normal adults[J]. Journal of Hunan College of Traditional Chinese Medicine, 2000, 20(3): 1-3.

WAMER H R, SWAN H J, CONNOLLY D C, et al. Quantitation of beat-to-beat changes in stroke volume from the aortic pulse contour in man[J]. Journal of Applied Physiology, 1953, 5(9): 495-507.

ELGENDI M. On the analysis of fingertip photoplethysmogram signals[J]. Current Cardiology Reviews, 2012, 8(1): 14-25.

CHANDRARATNA P A, SAN P S, SCHNEIDER R, et al. Implications of changes in amplitude and contour of the mercury strain gauge plethysmograph pulse tracing[J]. Heart, 1978, 40(8): 907-910.

De S G, DEVEREUX R B, CHINALI M, et al. Association of blood pressure with blood viscosity in american indians: The strong heart study[J]. Hypertension, 2005, 45(4): 625-30.

KOUNALAKIS S N and GELADAS N D. The role of pulse transit time as an index of arterial stiffness during exercise[J]. Cardiovascular Engineering, 2009, 9(3): 92-97.

SAITO M, MATSUKAWAM, ASADA T, et al. Noninvasive assessment of arterial stiffness by pulse wave analysis[J]. IEEE Transactions on Ultrasonics, Ferroelectronics, and Freqency Control, 2012, 59(11): 2411-2419.

SHI P, HU S, ZHU Y, et al. Insight into the dicrotic notch in photoplethysmographic pulses from the finger tip of young adults[J]. Journal of Medical Engineering Technology, 2009, 33(8): 628-633.

LAX H, FEINBERG A W, and COHEN B M. Studies of the arterial pulse wave. I. The normal pulse wave and its modification in the presence of human arteriosclerosis[J]. Journal of Chronic Diseases, 1956, 3(6): 618-631.

BABCHCHENKO A, DAVIDSON E, GINOSAR Y, et al. Photoplethysmographic measurement of changes in total and pulsatile tissue blood volume, following sympathetic blockade [J]. Physiological Measurement, 2001, 22(2): 389-397.

AWAD A A, HADDADIN A S, TANTAWY H, et al. The relationship between the photoplethysmographic waveform and systemic vascular resistance[J]. Journal of Clinical Monitoring and Computing, 2007, 21(6): 365-372.

LI J, JIN J, CHEN X, et al. Comparison of respiratory- induced variations in photoplethysmographic signals[J]. Physiological Measurement, 2010, 31(3): 415-425.

HISETH L, HOFF I E, HAGEN O A, et al. Respiratory variations in the photoplethysmographic waveform amplitude depend on type of pulse oximetry device[J]. Journal of Clinical Monitoring and Computing, 2016, 30(3): 317-325.

FOO J Y, LIM C S, and WILSON S J. Photoplethy smographic assessment of hemodynamic variations using pulsatile tissue blood volume[J]. Angiology, 2009, 59(6): 745-752. doi: 10.1177/0003319708314245.

JOHANSSON A. Neural network for photoplethysmographic respiratory rate monitoring[J]. Medical Biological Engineering Computing, 2003, 41(3): 242-248.

SZTAJZEL J. Heart rate variability: A noninvasive electrocardiographic method to measure the autonomic nervous system[J]. Swiss Medical Weekly, 2004, 134(35/36): 514-522.

LACKNER P, GUENGOER E, BEER R, et al. Photoplethy smography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions[J]. Physiological Measurement, 2010, 31(9): 1271-1290.

GU W B, POON C C Y, and ZHANG Y T. A novel parameter from PPG dicrotic notch for estimation of systolic blood pressure using pulse transit time[C]. International Summer School and Symposium on Medical Devices and Biosensors, HKSAR, 2008: 86-88.

SHALTIS P, REISNER A, and ASADA H. Calibration of the photoplethysmogram to arterial blood pressure: Capabilities and limitations for continuous pressure monitoring[J]. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, China, 2005, 4: 3970-3973. doi: 10.1109/IEMBS.2005. 1615331.

GU G, YANG L, LIU C, et al. Age and blood pressure associated changes in the Gaussian modeling characteristics of the photoplethysmographic pulse[J]. Experimental Clinical Cardiology, 2014, 20(9): 4943-4951.

徐可欣, 王继寸, 余辉, 等. 脉搏波时域特征与血压相关性的研究[J]. 中国医疗设备, 2009, 24(8): 42-45.

XU Kexin, WANG Jicun, YU Hui, et al. The research about the correlation between the pulse wave time-domain characteristics and blood pressure[J]. China Medical Devices, 2009, 24(8): 42-45.

罗志昌, 张松, 杨文鸣, 等. 脉搏波波形特征信息的研究[J]. 北京工业大学学报, 1996, 22(1): 71-79.

LUO Zhichang, ZHANG Song, YANG Wenming, et al. The research about pulse wave characteristic information[J]. Journal of Beijing Polytechnic University, 1996, 22(1): 71-79.

曾勇, 舒欢, 胡江平, 等. 基于BP神经网络的自适应伪最近邻分类[J]. 电子与信息学报, 2016, 38(11): 2774-2779. doi: 10.11999/JEIT160133.

ZENG Yong, SHU Hua, HU Jiangping, et al. Adaptive pseudo nearest neighbor classification based on BP neural network[J]. Journal of Electronics Information Technology, 2016, 38(11): 2774-2779. doi: 10.11999/JEIT160133.

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