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.
Feature Extraction and Classification of ECG Signal Based on The Standard Extended Wavelet Transform Technique: Cardiology Based Telemedicine
