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Study on the classification algorithm of degree of arteriosclerosis basedon fuzzy pattern recognition

机译:基于模糊模式识别的动脉硬化程度分类算法研究

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Pulse wave of human body contains large amount of physiological and pathological information, so the degree of arteriosclerosis classification algorithm is study based on fuzzy pattern recognition in this paper. Taking the human's pulse wave as the research object, we can extract the characteristic of time and frequency domain of pulse signal, and select the parameters with a better clustering effect for arteriosclerosis identification. Moreover, the validity of characteristic parameters is verified by fuzzy ISODATA clustering method (FISOCM). Finally, fuzzy pattern recognition system can quantitatively distinguish the degree of arteriosclerosis with patients. By testing the 50 samples in the built pulse database, the experimental result shows that the algorithm is practical and achieves a good classification recognition result.
机译:人体的脉搏挥发含有大量的生理和病理信息,因此动脉硬化分类程度是基于本文模糊模式识别的研究。以人的脉搏波作为研究对象,我们可以提取脉冲信号的时间和频域的特征,并选择具有更好的聚类效果的参数,用于动脉硬化识别。此外,通过模糊ISODATA聚类方法(FISOCM)验证了特征参数的有效性。最后,模糊模式识别系统可以定量地与患者的动脉硬化程度区分。通过在内置脉冲数据库中测试50个样本,实验结果表明该算法实用,实现了良好的分类识别结果。

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