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首页> 外文会议>International Conference on Frontiers of Intelligent Computing : Theory and Applications >Fuzzy Membership Estimation Using ANN: A Case Study in CTG Analysis
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Fuzzy Membership Estimation Using ANN: A Case Study in CTG Analysis

机译：使用ANN的模糊会员估算：CTG分析的案例研究

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The Cardiotocograph (CTG) is being used by the obstetricians since 1960s as a means for recording (graphy) the heart beat (cardio) and the uterine contraction pressure (toco) of the mother, to evaluate the well being of the fetus. One of the major features of fetal heart rate (FHR) is its baseline,the accurate classification which is of utmost importance as all the other parameters of CTG rely on it. Inherent vagueness in the assessment given by the physicians can probably be modeled using fuzzy logic. It is one of the most trusted tools to handle uncertainty intrinsically present in the linguistic expression of human. The main challenge in designing a fuzzy logic based system is to design its membership function. In this paper we have presented a ANN based technique for the design of Fuzzy Membership Function (FMF) of FHR and used it in Fuzzy Unordered Rule Induction Algorithm (FURIA) in order to classify the CTG. The results obtained show significant improvement in classification over non FMF based technique.
机译：自20世纪60年代以自20世纪60年代以来，妇产科（CTG）是用于录制（汇总）心跳（心脏）和母亲的子宫收缩压力（TOCO）的手段，以评估胎儿的福祉。胎儿心率（FHR）的主要特征之一是其基线，准确的分类是最重要的，因为CTG的所有其他参数依赖于此。医生提供的评估中的固有模糊可能是使用模糊逻辑进行建模的。它是处理人类语言表达中本质上存在的不确定性的最值得信赖的工具之一。设计模糊基于逻辑系统的主要挑战是设计其成员函数。在本文中，我们介绍了基于ANN技术的FHR的模糊会员函数（FMF）的技术，并以模糊的无序规则感应算法（FURIA）用于分类CTG。得到的结果显示出基于非FMF技术的分类显着改善。

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