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Wavelets and fuzzy relational classifiers: A novel spectroscopy analysis system for pediatric metabolic brain diseases

机译:小波和模糊关系分类器:一种用于儿科代谢性脑疾病的新型光谱分析系统

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摘要

A suspected metabolic disorder presents a difficult challenge to the physician and the patient. We have developed a fully automated system in order to analyze and classify the magnetic resonance spectroscopy signals of patients with metabolic brain diseases. We utilized wavelets to extract signal features and in the time-frequency representations to optimize the feature extraction procedure. Novel fuzzy membership functions and a fuzzy relational classifier were designed to categorize the metabolic brain diseases in children using the information obtained from the feature extraction routine. The sensitivity (Se) and the positive predictivity (PP) of 88.26% and 91.04% in extracting features and 89.66% and 100%, respectively, in detecting metabolic brain diseases has been achieved.
机译:可疑的代谢紊乱给医师和患者带来了艰巨的挑战。我们已经开发了一套全自动系统,以分析和分类代谢性脑病患者的磁共振波谱信号。我们利用小波提取信号特征,并在时频表示中优化特征提取过程。设计了新颖的模糊隶属函数和模糊关系分类器,使用从特征提取例程获得的信息对儿童的代谢性脑病进行分类。在检测代谢性脑疾病中,提取特征的敏感性(Se)和阳性预测性(PP)分别为88.26%和91.04%,检测代谢性脑病的敏感性分别为89.66%和100%。

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