光谱学与光谱分析 |
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Study on Indicator Densitometry Determination Method of Hemodynamic Parameters |
LIU Guang-da1, ZHOU Run-dong1, ZHA Yu-tong1, CAI Jing1, NIU Jun-qi2*, GAO Pu-jun2, LIU Li-li2 |
1. College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China 2. First Hospital, Jilin University, Changchun 130021, China |
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Abstract Measurement for hemodynamic parameters has always been a hot spot of clinical research. Methods for measuring hemodynamic parameters clinically have the problems of invasiveness, complex operation and being unfit for repeated measurement. To solve the problems, an indicator densitometry analysis method is presented based on near-infrared spectroscopy (NIRS) and indicator dilution theory, which realizes the hemodynamic parameters measured noninvasively. While the indocyanine green (ICG) was injected into human body, circulation carried the indicator mixing and diluting with the bloodstream. Then the near-infrared probe was used to emit near-infrared light at 735, 805 and 940 nm wavelengths through the sufferer’s fingertip and synchronously capture the transmission light containing the information of arterial pulse wave. By uploading the measured data, the computer would calculate the ICG concentration, establish continuous concentration curve and compute some intermediate variables such as the mean transmission time (MTT) and the initial blood ICG concentration (ct0). Accordingly Cardiac Output (CO) and Circulating Blood Volume (CBV) could be calculated. Compared with the clinical “gold standard” methods of thermodilution and I-131 isotope-labelling method to measure the two parameters by clinical controlled trials, ten sets of data were obtained. The maximum relative errors of this method were 8.88% and 4.28% respectively, and both of the average relative errors were below 5%. The result indicates that this method can meet the clinical accuracy requirement and can be used as a noninvasive, repeatable and applied solution for clinical hemodynamic parameters measurement.
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Received: 2014-11-19
Accepted: 2015-02-25
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Corresponding Authors:
NIU Jun-qi
E-mail: junqiniu@aliyun.com
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[1] LIU You-jun, QIAO Ai-ke(刘有军,乔爱科). Journal of Medical Biomechanics(医用生物力学),2012, 27(5):475. [2] BAI Fan, LIU You-jun, XIE Jin-sheng, et al(白 帆,刘有军,谢进生,等). Journal of Medical Biomechanics(医用生物力学),2013, 28(6):677. [3] ZHAO Ze, WANG Ling, PAN Song-xin, et al(赵 泽,王 玲,潘颂欣,等). Chinese Journal of Biomedical Engineering(中国生物医学工程学报), 2010, 29(4): 619. [4] Reuter Daniel A, Huang Cecil, Edrich Thomas, et al. Anesthesia & Analgesia,2010, 110(3):799. [5] ZHANG Hui, JIANG Wei(张 晖,江 伟). Foreign Medical Sciences (Anesthesiogy and Resuscitation)(国外医学·麻醉学与复苏分册),2004, 25(6):368. [6] YU Bu-wei(于布为). China Medical News(中华医学信息导报),2004, 19(10):19. [7] SUN Xin, LIU Chang-chun, ZHAO Yu-juan, et al(孙 欣, 刘常春, 赵玉娟, 等). Journal of Optoelectronics·Laser(光电子·激光),2010, 21(8):1214. [8] Schuster A. Astrophysical Journal, 1905, 21(1):1. [9] Iijima T, Aoyagi T, Iwao Y, et al. J. Clin. Monit.,1997, 13(2):81. [10] Masahiko Kanda, Shin-ichiro Niwa. Apply Optics,1992, 31(31):6668. [11] Marije Reekers, Mischa J, Fred Boer, et al. Anesthesia & Analgesia,2009, 209(2): 441. |
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