Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms
Pattern Recognition Letters
A decision support system based on support vector machines for diagnosis of the heart valve diseases
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
Support vector machines for detection of electrocardiographic changes in partial epileptic patients
Engineering Applications of Artificial Intelligence
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
Support Vector Machines for Pattern Classification
Support Vector Machines for Pattern Classification
Neural network and wavelet average framing percentage energy for atrial fibrillation classification
Computer Methods and Programs in Biomedicine
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In this study we have proposed and compared use of CWT (Continues Wavelet Transform) with two powerful data transformation techniques DWT (Discrete Wavelet Transform), and DCT (Discrete Cosine Transform) which have already been in use, in order to improve the capability of two pattern classifiers in ECG arrhythmias classification. The classifiers under examination are MLP (Multi-Layered Perceptron, a conventional neural network) and SVM (Support Vector Machine). The training or learning algorithms used in MLP and SVM are BackPropagation (BP) and Kernel-Adatron (K-A), respectively. The ECG signals taken from MIT-BIH arrhythmia database are used to classify four different arrhythmias together with normal ECG. The output of MLP and SVM classifiers in terms of training performance, testing performance or generalization ability and training time are compared. MLP and SVM training and testing stages have been carried out twice. At first, only one lead (II) is used, and then a second ECG lead (V1) has been added to the training and testing datasets. Three feature extraction techniques are applied separately to datasets before classification. The results show that selection of the best feature extraction method will depend on the substantial value considered for training time, training and testing performance. This is stated because when applying MLP or SVM, addition of CWT and DCT will show the advantage only when training performance and testing performance are important, respectively. Generally speaking only testing performance with single lead for MLP shows superiority over SVM.