Hardalaç Firat
Department of Computer Engineering, Faculty of Engineering, Kirikkale University, Kirikkale, Turkey.
J Med Syst. 2008 Apr;32(2):137-45. doi: 10.1007/s10916-007-9116-6.
Transcranial Doppler signals recorded from cerebral vessels of 110 patients were transferred to a personal computer by using a 16 bit sound card. Spectral analyses of Transcranial Doppler signals were performed for determining the Multi Layer Perceptron (MLP) neural network and neuro Ankara-fuzzy system inputs. In order to do a good interpretation and rapid diagnosis, FFT parameters of Transcranial Doppler signals classified using MLP neural network and neuro-fuzzy system. Our findings demonstrated that 92% correct classification rate was obtained from MLP neural network, and 86% correct classification rate was obtained from neuro-fuzzy system.
通过使用16位声卡,将从110名患者脑血管记录的经颅多普勒信号传输到个人计算机。对经颅多普勒信号进行频谱分析,以确定多层感知器(MLP)神经网络和神经安卡拉模糊系统的输入。为了进行良好的解释和快速诊断,使用MLP神经网络和神经模糊系统对经颅多普勒信号的快速傅里叶变换(FFT)参数进行分类。我们的研究结果表明,MLP神经网络的正确分类率为92%,神经模糊系统的正确分类率为86%。