采用S变换特征选择方法的心律失常分类

针对短时傅里叶变换与小波变换对心电图（Electrocardiogram,ECG）信号特征提取不足以及心律失常识别困难的问题,提出了一种基于S变换特征选择的心律失常分类算法。首先对ECG信号进行S变换,并从幅值和相位两个角度提取ECG信号的时频特征,与形态特征和RR间隔组成原始特征向量。然后将遗传算法与支持向量机（Support vector machine,SVM）结合组成Wrapper式特征选择方法,并在其中融入ReliefF算法,即采用ReliefF算法计算特征权重,并根据特征权重大小来指导遗传算法种群初始化,遗传算法以SVM的分类性能作为适应度函数来搜索特征子集。最后使用"一对多"（One against all,OAA）SVM对MIT-BIH心律失常数据库8种类型心拍进行分类。实验结果表明,该算法达到了较好的分类效果,灵敏度、特异性和准确率分别为96.14%,99.75%和99.81%。

Hot Topic Detection Based on Short Text Information Flow Arrhythmia Classification Based on Feature Selection Method of S-transform

Short time Fourier transform and wavelet transform are not effective in extracting features of electrocardiogram (ECG) signal for arrhythmia detection. Therefore,a novel algorithm based on the feature selection of S-transform is proposed for arrhythmia classification. First, ECG signals are processed by S-transform, and the time-frequency features are extracted from both the amplitude and the phase of ST results. Then, time-frequency features, morphological features, and RR interval are combined as the original feature vector. Second, the genetic algorithm (GA) and support vector machine (SVM) are combined as a Wrapper approach to search an optimal feature subset. The feature weights are computed by ReliefF algorithm, and the initialization of genetic population depends on the feature weights. Moreover,GA searches an optimal feature subset using classification performance as the fitness function. Finally, a multi-SVM model with one against all (OAA) strategy is built for the classification of eight types of ECG beats from the MIT-BIH arrhythmia database. Experimental results indicate that the proposed approach has the best performance among other state-of-the-art approaches, and the sensitivity, specificity, and accuracy reach 96.14%, 99.75%, and 99.81%, respectively.