一種基于多參數(shù)融合的無(wú)袖帶式連續(xù)血壓測(cè)量方法的研究

徐志紅; 方震; 陳賢祥; 覃力; 杜利東; 趙湛; 劉杰昕

doi:10.11999/JEIT170238

一種基于多參數(shù)融合的無(wú)袖帶式連續(xù)血壓測(cè)量方法的研究

doi: 10.11999/JEIT170238 cstr: 32379.14.JEIT170238

2.
(中國(guó)科學(xué)院電子學(xué)研究所北京 100190)
3.
(北京天壇醫(yī)院北京 100050)

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61302033)，北京市自然科學(xué)基金(Z16003)，國(guó)家重點(diǎn)研發(fā)計(jì)劃(2016YFC1304302)

計(jì)量
- 文章訪問(wèn)數(shù): 1684
- HTML全文瀏覽量: 177
- PDF下載量: 218
- 被引次數(shù): 0
出版歷程
- 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: 針對(duì)現(xiàn)有基于脈搏波傳輸時(shí)間的無(wú)創(chuàng)連續(xù)性血壓測(cè)量算法精度不高的問(wèn)題，該文綜合考慮心電信號(hào)和血氧容積波與血壓變化的相關(guān)性，提出一種基于BP神經(jīng)網(wǎng)絡(luò)的無(wú)創(chuàng)連續(xù)性血壓測(cè)量方法。該文首先利用改進(jìn)的心電信號(hào)算法提取出心電信號(hào)的R點(diǎn)，利用差分、閾值的方法提取出血氧容積波的特征參數(shù)，再經(jīng)過(guò)特征解析，提取出與血壓相關(guān)的15維特征向量，構(gòu)建基于BP神經(jīng)網(wǎng)絡(luò)的血壓計(jì)算模型，計(jì)算出逐拍的血壓值。該方法在天壇醫(yī)院等單位進(jìn)行了醫(yī)學(xué)臨床比對(duì)測(cè)試，并通過(guò)因子分析法分析了15個(gè)特征參數(shù)的權(quán)重比。實(shí)驗(yàn)證明：在預(yù)測(cè)血壓上，脈搏波傳輸時(shí)間的權(quán)重，大于相鄰特征點(diǎn)之間的時(shí)間信息權(quán)重，大于脈搏波面積信息權(quán)重，大于脈搏波幅值信息權(quán)重；該方法精度優(yōu)于其它相近方法，單次測(cè)量的舒張壓和收縮壓誤差的平均值標(biāo)準(zhǔn)差分別是-1.576.12 mmHg和-0.624.82 mmHg，重復(fù)測(cè)量誤差的平均值標(biāo)準(zhǔn)差分別是-2.125.10 mmHg和-2.524.41 mmHg。收縮壓和舒張壓的測(cè)量精度均達(dá)到了BHS血壓標(biāo)準(zhǔn)的Grade A類(lèi)和AAMI標(biāo)準(zhǔn)。
- 可穿戴式技術(shù) /
- 連續(xù)血壓 /
- 多參數(shù)融合 /
- 神經(jīng)網(wǎng)絡(luò)
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

HTML全文

參考文獻(xiàn)(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. 基于容積脈搏波血流多參數(shù)測(cè)量的研究[D]. [博士論文], 南方醫(yī)科大學(xué), 2010.

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

吳秋玲, 吳蒙. 基于小波變換的語(yǔ)音信息隱藏新方法[J]. 電子與信息學(xué)報(bào), 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.

吳光文, 王昌明, 包建東, 等. 基于自適應(yīng)閾值函數(shù)的小波閾值去噪方法[J]. 電子與信息學(xué)報(bào), 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, 李廣軍, 李強(qiáng). 一種基于迭代短卷積算法的低復(fù)雜度并行FIR濾波器結(jié)構(gòu)[J]. 電子與信息學(xué)報(bào), 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.

黃聰, 劉寅. 基于多普勒頻偏估計(jì)的單幀圖像低速運(yùn)動(dòng)目標(biāo)檢測(cè)方法[J]. 電子與信息學(xué)報(bào), 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.

黃曉霞, 羅勝欽, 陸明達(dá). 人工神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)穩(wěn)定的自適應(yīng)IIR濾波器[J]. 電子與信息學(xué)報(bào), 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.

許華健, 楊志偉, 廖桂生, 等. 一種穩(wěn)健的非均勻雜波協(xié)方差矩陣估計(jì)方法[J]. 電子與信息學(xué)報(bào), 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.

韓慶陽(yáng), 王曉東, 李丙玉, 等. EEMD在同時(shí)消除脈搏血氧檢測(cè)中脈搏波信號(hào)高頻噪聲和基線漂移中的應(yīng)用[J]. 電子與信息學(xué)報(bào), 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.

唐洪榮, 沈民奮, 李斌. 周期緊支撐徑向基函數(shù)對(duì)BEMD的優(yōu)化[J]. 電子與信息學(xué)報(bào), 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, 王繼寸, 余輝, 等. 脈搏波時(shí)域特征與血壓相關(guān)性的研究[J]. 中國(guó)醫(yī)療設(shè)備, 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.

羅志昌, 張松, 楊文鳴, 等. 脈搏波波形特征信息的研究[J]. 北京工業(yè)大學(xué)學(xué)報(bào), 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, 舒歡, 胡江平, 等. 基于BP神經(jīng)網(wǎng)絡(luò)的自適應(yīng)偽最近鄰分類(lèi)[J]. 電子與信息學(xué)報(bào), 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.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問(wèn)統(tǒng)計(jì)