| 光谱学与光谱分析 |
|
|
|
|
|
| Study on the Application of Empirical Mode Decomposition to Noninvasive Hemoglobin Measurement by NIRS |
| FAN Yi-chen1, 2, LU Qi-peng1*, DING Hai-quan1, GAO Hong-zhi1, CHEN Xing-dan1 |
1. State Key Lab of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China |
|
|
|
|
Abstract To increase the signal-to-noise ratio (SNR) of human near infrared (NIR) spectra, so as to improve the stability and precision of calibration model, the empirical mode decomposition (EMD) method was applied. Eighty-one fingertip absorption curves were collected, with the corresponding clinical examination results obtained immediately. By means of outliers detection and removal, finally 78 samples were determined as the research objects. A three-layer back-propagation artificial neutron network (BP-ANN) model was established and worked for prediction. The results turned out that, through EMD method, the prediction correlation coefficient increased greatly from 0.74 to 0.87. RMSEP was reduced from 12.85 to 8.08 g·L-1. Other indexes were also obviously improved. The overall results sufficiently demonstrate that it is feasible to use EMD method for high SNR pulse wave signals, thus improving the performance of noninvasive hemoglobin calibration models. The application of EMD method can help promote the development of noninvasive hemoglobin monitoring technology.
|
|
Received: 2012-06-01
Accepted: 2012-08-20
|
|
|
|
Corresponding Authors:
LU Qi-peng
E-mail: luqipeng@126.com
|
|
[1] Timm U, Lewis E, McGrath D, et al. Procedia Engineering, 2010, 5: 488.
[2] Zhou Y, Zheng C, Cao H, et al. Biochemical and Biophysical Research Communications, 2012, 420: 205.
[3] Causey M W, Miller M D S, et al. The American Journal of Surgery, 2011, 201: 592.
[4] Jeon Kyejin, Kim Sujin, Park kunkook. Journal of Biomedical Optics, 2002, 7(1): 45.
[5] LU Qi-peng, CHEN Cong, PENG Zhong-qi(卢启鹏，陈 丛，彭忠琦). Optics and Precision Engineering(光学精密工程), 2012, 20(4): 873.
[6] CHEN Xing-dan(陈星旦). Optics and Precision Engineering(光学精密工程), 2008, 16(5): 759.
[7] LU Wan-zhen(陆婉珍). Modern Near-Infrared Spectroscopy Analytical Technology, 2nd ed(现代近红外光谱分析技术，第2版). Beijing: China Petrochemical Press(北京：中国石化出版社), 2006.
[8] CHEN Cong, LU Qi-peng, PENG Zhong-qi(陈 丛，卢启鹏，彭忠琦). Acta Optica Sinica(光学学报), 2012, 32(5): 0530001-1.
[9] Huang N E, Shen Z, Long S R, et al. Proceedings of the Royal Society A, 1998, 454: 903.
[10] Huang N E, Shen Z, Long S R. Annual Review of Fluid Mechanics, 1999, 31: 417.
[11] Bao L, Dai P H. Journal of Systems Engineering, 1999, 14(4): 338. |
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [3] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [4] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [5] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [6] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
| [9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [10] |
WANG Zhen-ni1, KANG Zhi-wei1*, LIU Jin2, ZHANG Jie2. A Solar Spectral Doppler Redshift Velocity Measurement Method Based on Adaptive EMD-NDFT[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3475-3482. |
| [11] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
| [12] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [13] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [14] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [15] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
|
|
|
|
