Polat Kemal, Kara Sadik, Latifoğlu Fatma, Güneş Salih
Department of Electrical & Electronics Engineering, Selcuk University, 42075, Konya, Turkey.
Ann Biomed Eng. 2007 May;35(5):724-32. doi: 10.1007/s10439-007-9289-7. Epub 2007 Mar 27.
Carotid Artery Doppler Signals were recorded from 114 subjects, 60 of whom had Atherosclerosis disease while the rest were healthy controls. Diagnosis of Atherosclerosis from Carotid Artery Doppler Signals was conducted using Fuzzy weighted pre-processing and Least Square Support Vector Machine (LSSVM). First, in order to determine the LSSVM inputs, spectral analysis of Carotid Artery Doppler Signals was performed via Autoregressive (AR) modeling. Then, fuzzy weighted pre-processing based is proposed expert system, applied to inputs obtained from spectral analysis of Carotid Artery Doppler Signals. LSSVM was used to detect Atherosclerosis from Carotid Artery Doppler Signals. All data set were obtained from Carotid Artery Doppler Signals of healthy subjects and subjects suffering from Atherosclerosis disease. The employed expert system has achieved 100% classification accuracy using a 10-fold Cross Validation (CV) method.
记录了114名受试者的颈动脉多普勒信号，其中60人患有动脉粥样硬化疾病，其余为健康对照。使用模糊加权预处理和最小二乘支持向量机（LSSVM）从颈动脉多普勒信号中诊断动脉粥样硬化。首先，为了确定LSSVM的输入，通过自回归（AR）建模对颈动脉多普勒信号进行频谱分析。然后，提出基于模糊加权预处理的专家系统，并将其应用于从颈动脉多普勒信号频谱分析中获得的输入。使用LSSVM从颈动脉多普勒信号中检测动脉粥样硬化。所有数据集均来自健康受试者和患有动脉粥样硬化疾病受试者的颈动脉多普勒信号。所采用的专家系统使用10折交叉验证（CV）方法实现了100%的分类准确率。
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