Department of Computer Engineering, Selçuk University, Konya, Turkey.
Comput Methods Programs Biomed. 2012 Sep;107(3):598-609. doi: 10.1016/j.cmpb.2011.03.013. Epub 2011 Apr 27.
A transcranial Doppler (TCD) is a non-invasive, easy to apply and reliable technique which is used in the diagnosis of various brain diseases by measuring the blood flow velocities in brain arteries. This study aimed to classify the TCD signals, and feature ranking (information gain - IG) and dimension reduction methods (principal component analysis - PCA) were used as a hybrid to improve the classification efficiency and accuracy. In this context, each feature within the feature space was ranked depending on its importance for the classification using the IG method. Thus, the less important features were ignored and the highly important features were selected. Then, the PCA method was applied to the highly important features for dimension reduction. As a result, a hybrid feature reduction between the selection of the highly important features and the application of the PCA method on the reduced features were achieved. To evaluate the effectiveness of the proposed method, experiments were conducted using a support vector machine (SVM) classifier on the TCD signals recorded from the temporal region of the brain of 82 patients, as well as 24 healthy people. The experimental results showed that using the IG and PCA methods as a hybrid improves the classification efficiency and accuracy compared with individual usage.
经颅多普勒（TCD）是一种非侵入性、易于应用且可靠的技术，通过测量脑动脉中的血流速度，用于诊断各种脑部疾病。本研究旨在对 TCD 信号进行分类，并使用特征排序（信息增益 - IG）和降维方法（主成分分析 - PCA）的混合方法来提高分类效率和准确性。在这种情况下，使用 IG 方法根据每个特征对分类的重要性对特征空间中的每个特征进行排序。因此，忽略了不太重要的特征，选择了非常重要的特征。然后，将 PCA 方法应用于重要特征的降维。结果，在选择重要特征和在降维后的特征上应用 PCA 方法之间实现了混合特征降维。为了评估所提出方法的有效性，在 82 名患者和 24 名健康人的大脑颞区记录的 TCD 信号上，使用支持向量机（SVM）分类器进行了实验。实验结果表明，与单独使用相比，使用 IG 和 PCA 方法的混合可以提高分类效率和准确性。
