光谱学与光谱分析, 2012, 32 (9): 2508, 网络出版: 2012-09-26
基于BP神经网络的人体血液中红细胞浓度无创检测
Noninvasive Measurement of the Human RBC Concentration Based on BP NN Model
BP神经网络 无创测量 红细胞浓度 动态光谱 BP neural network Non-invasive measurement Red blood cells (RBC) concentration Dynamic spectrum
摘要
采用BP神经网络算法模型对人体血液红细胞浓度进行无创检测。 对获取的动态光谱数据和红细胞实测值利用BP神经网络进行建模分析, 校正集输出对期望值的跟踪较好, 相关系数R达到了0.993, 用建立起的BP神经网络模型去检验预测集输出值, 得到预测集的相对误差最大为4.7%, 平均相对误差为2.1%, 预测能力较为理想。 结果表明: 用BP神经网络模型能够较准确的处理动态光谱数据和人体红细胞实际值的非线性关系, 提高了血液成分无创测量在临床上应用的可行性, 具有较高的应用价值。
Abstract
In the present article, the BP neural network’s arithmetic model is applied to the noninvasive detection of the concentration of the red blood cell of human body. Due to the use of BP neural network in the modeling and analysis of the dynamic spectrum data and the actual measured value of the red blood cell, the authors get a better result which refers to that the output value tracks the expected result very well. The related coefficient R can reach 0.993. When predicting the output value in the way of the BP neural network model, the maximal relative error is only 4.7%, average relative error is 2.1%, so the authors can say that it has more ideal prediction ability. The experimental result shows that the BP neural network model can be accurate in dealing with the nonlinear relation between the dynamic spectrum data and human erythrocyte practical value and it can make the method of noninvasive blood analysis more useful in clinical application. So it has a high application value.
张宝菊, 雷晴, 李刚, 林凌, 王慧泉, Jean Gao. 基于BP神经网络的人体血液中红细胞浓度无创检测[J]. 光谱学与光谱分析, 2012, 32(9): 2508. ZHANG Bao-ju, LEI Qing, LI Gang, LIN Ling, WANG Hui-quan, Jean Gao. Noninvasive Measurement of the Human RBC Concentration Based on BP NN Model[J]. Spectroscopy and Spectral Analysis, 2012, 32(9): 2508.