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Classification of Aorta Insufficiency and Stenosis Using Neuro-Fuzzy System

机译:使用神经模糊系统对主动脉瓣关闭不全和狭窄进行分类

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摘要

Cardiac Doppler signals recorded from aorta valve of 60 patients were transferred to a personal computer by using a 16 bit sound card. The fast Fourier transform (FFT) method was applied to the recorded signal from each patient. Since FFT method inherently cannot offer a good spectral resolution at jet blood flows such as cardiac Doppler signals, it sometimes causes wrong interpretation. In order to do a good interpretation and rapid diagnosis, cardiac Doppler blood flow signals were statistically arranged and then classified using neuro-fuzzy system. The NEFCLASS model, which is used to create a fuzzy classification system from data, was used. The classification results show that neuro-fuzzy system offers best results in the case of diagnosis.
机译:通过使用16位声卡将从60位患者的主动脉瓣记录的心脏多普勒信号传输到个人计算机。快速傅里叶变换(FFT)方法应用于来自每个患者的记录信号。由于FFT方法固有地不能在喷射血流(例如心脏多普勒信号)上提供良好的频谱分辨率,因此有时会引起错误的解释。为了进行良好的解释和快速诊断,对心脏多普勒血流信号进行统计学处理,然后使用神经模糊系统对其进行分类。使用了NEFCLASS模型,该模型用于根据数据创建模糊分类系统。分类结果表明,在诊断情况下,神经模糊系统可提供最佳结果。

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