首页> 外文会议>WSEAS International Conference on Biomedical Electronics and Biomedical Informatics >A Non-Linear Multivariable Regression Method for the Investigation of the Correlation Between Central Corneal Thickness and HRTII Optic Nerve Head Topographic Measurements in Glaucoma Patients
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A Non-Linear Multivariable Regression Method for the Investigation of the Correlation Between Central Corneal Thickness and HRTII Optic Nerve Head Topographic Measurements in Glaucoma Patients

机译:一种非线性多变量回归荧光肿患者中央角膜厚度与HRTII视神经头地形测量的相关性研究

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In this paper a new non-linear multivariable regression method is presented to investigate the existence of correlation between the central corneal thickness (CCT) and the optic nerve head (ONH) topographic measurements in patients with glaucoma. Specifically, the model is supplied with Heidelberg Retina Tomograph II data including optic nerve head topographic parameters (i.e. disc area, etc) as well as independent parameters (i.e. patient age, refraction, etc), constituting the proposed algorithm's input variables. Each input variable is transformed using non-linear functions, such as x~a, 1/x , ln (x) , e~(-x), and new are created. The algorithm performs an extensive search in order to select the appropriate transformation functions of input variables to be used, by taking into consideration the correlation analysis of the transformed input variables. Following all possible non-linear multivariable models are tested and the best is chosen according to the correlation index R~2 between the experimental and predicted values of the central corneal thickness satisfying the F-test and t-tests criterions synchronously. The results from the application of the described method are presented for ninety three eyes with open angle glaucoma and they are also compared to those obtained from the application of standard regression methods.
机译:本文提出了一种新的非线性多变量回归方法,以研究青光眼患者中央角膜厚度(CCT)和视神经头(ONH)地形测量的相关性。具体而言,该模型提供了海德堡视网膜断层扫描II数据,包括视神经头部地形参数(即盘区域等)以及独立参数(即患者年龄,折射等),构成所提出的算法的输入变量。使用非线性函数进行转换每个输入变量,例如x〜a,1 / x,ln(x),e〜(-x)和新的。该算法通过考虑转换的输入变量的相关性分析,执行广泛的搜索以选择要使用的输入变量的适当变换功能。在测试所有可能的非线性多变量型号之后,根据所在的中心角膜厚度的实验和预测值之间的相关指数R〜2选择最佳的非线性多变量型号,其同步地满足F-Test和T-Tests标准。由所描述的方法的应用的结果呈现出九十三个眼睛,其开口角胶质瘤也与由标准回归方法的应用中获得的那些相比。

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