一种确定麻醉深度的新型混合方法:ReliefF特征选择与随机森林算法(ReliefF+RF)相结合

M. Peker, Ayse Arslan, B. Şen, F. Çelebi, Abdulkadir But
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引用次数: 29

摘要

麻醉深度在外科手术中是一个非常重要的问题。麻醉深度的确定是一项耗时且困难的任务,由专家来完成。本研究旨在确定一种能够对脑电数据进行高精度自动分类的方法,从而为专家的判断过程提供帮助。该研究分为三个阶段:脑电信号特征提取、特征选择和分类。在特征提取阶段,共获得41个特征参数。特征选择阶段是消除冗余属性、提高预测精度和计算时间性能的重要环节。有效的特征选择算法,如最小冗余最大相关性(mRMR);ReliefF;在特征选择阶段,首选顺序前向选择(SFS),以选择一组最能代表脑电信号的特征。这些获得的特征被用作分类算法的输入参数。在分类阶段,有六种不同的分类算法,如随机森林(RF);前馈神经网络(FFNN);决策树算法(C4.5);支持向量机;朴素贝叶斯;和径向基函数神经网络(RBF)是分类问题的首选方法。比较了不同分类算法的计算次数和准确率。实验结果表明,将ReliefF算法得到的属性与RF分类器结合使用,其他分类器的分类效果更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel hybrid method for determining the depth of anesthesia level: Combining ReliefF feature selection and random forest algorithm (ReliefF+RF)
Depth of anesthesia is a matter of great importance in surgery. Determination of depth of anesthesia is a time consuming and difficult task carried out by experts. This study aims to decide a method that can classify EEG data automatically with a high accuracy and, so will help the experts for determination process. This study consists of three stages: feature extraction of EEG signals, feature selection, and classification. In the feature extraction stage, 41 feature parameters are obtained. Feature selection stage is important to eliminate redundant attributes and improve prediction accuracy and performance in terms of computational time. Effective feature selection algorithms such as minimum redundancy maximum relevance (mRMR); ReliefF; and Sequential Forward Selection (SFS) are preferred at the feature selection stage to select a set of features which best represent EEG signals. These obtained features are used as input parameters of the classification algorithms. At the classification stage, six different classification algorithms such as random forest (RF); feed-forward neural network (FFNN); C4.5 decision tree algorithm (C4.5); support vector machines (SVM); naive bayes; and radial basis function neural network (RBF) are preferred to classify the problem. A comparison is provided between computation times and accuracy rates of these different classification algorithms. The experimental results show that better results according to other classifiers when the obtained attributes by ReliefF algorithm are used with RF classifier.
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