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首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >COMPARISON OF TYPE-2 FUZZY CLUSTERING-BASED CASCADE CLASSIFIER MODELS FOR ECG ARRHYTHMIAS
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COMPARISON OF TYPE-2 FUZZY CLUSTERING-BASED CASCADE CLASSIFIER MODELS FOR ECG ARRHYTHMIAS

机译：心电图心律失常基于2型模糊聚类的分级分类器模型的比较

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摘要

The aim of this study is to present a comparison of the novel cascade classifier models based on fuzzy clustering and feature extraction techniques according to efficiency. These models are composed of three subsystems: The first subsystem is constituted by fuzzy clustering technique to choose the best patterns that ideally show its class attributes in dataset. The second subsystem consists of discrete wavelet transform (DWT) which realizes feature extraction procedure on selected patterns by using fuzzy c-means clustering. The last subsystem implements the classification of extracted features for each pattern using classification algorithm. In this paper, type-2 fuzzy c-means (T2FCM) clustering is used in the first subsystem of the proposed classification models and the new training set is obtained. In the second subsystem, the features of the obtained new training set are extracted with DWT; hence, three different feature sets along with different number of features are formed using Daubechies-2 wavelet function. In the last subsystem of the model, the feature sets are classified using classification algorithm. Here, two different classification algorithms, neural network (NN) and support vector machine (SVM), are used for comparison. Thus, two classification models are implemented and named as T2FCWNN (classifier is NN) and T2FCWSVM (classifier is SVM), respectively. This proposed classifier models have been applied to classify electrocardiogram (ECG) signals. One of the goals of this study is to present a fast and efficient classifier. For this reason, high accuracy rate is been aimed for classification of RR intervals in ECG signal. So, we have utilized T2FCM and WTs to improve the performance of the classification algorithms. Both training and testing set for classifier models have included 12 ECG signal classes. Well-known back propagation algorithm has been used for training of neural networks (NNs). The best testing results have been obtained with 99% recognition accuracy with T2FCWNN-2.
机译：本研究的目的是根据效率对基于模糊聚类和特征提取技术的新型叶栅分类器模型进行比较。这些模型由三个子系统组成：第一个子系统是通过模糊聚类技术构成的，以选择理想的模式以在数据集中理想地显示其类别属性。第二个子系统由离散小波变换（DWT）组成，该离散小波变换通过使用模糊c均值聚类实现对选定模式的特征提取过程。最后一个子系统使用分类算法为每个模式实现提取特征的分类。本文在提出的分类模型的第一个子系统中使用了2型模糊c均值（T2FCM）聚类，并获得了新的训练集。在第二个子系统中，使用DWT提取获得的新训练集的特征。因此，使用Daubechies-2小波函数形成了三个不同的特征集以及不同数量的特征。在模型的最后一个子系统中，使用分类算法对特征集进行分类。在这里，两种不同的分类算法，神经网络（NN）和支持向量机（SVM），用于比较。因此，实现了两个分类模型，分别命名为T2FCWNN（分类器为NN）和T2FCWSVM（分类器为SVM）。该提议的分类器模型已应用于对心电图（ECG）信号进行分类。这项研究的目标之一是提出一种快速有效的分类器。因此，针对ECG信号中的RR间隔的分类，以高准确率为目标。因此，我们利用T2FCM和WT来提高分类算法的性能。分类器模型的训练和测试集都包括12种ECG信号类别。众所周知的反向传播算法已用于神经网络（NN）的训练。 T2FCWNN-2以99％的识别精度获得了最佳的测试结果。

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