Classification of heart arrthymias by using wavelet and merged wavelet packet transforms

In this work efficiency of feature extraction methods based on linear wavelet transform and merged wavelet packets technique are evaluated relatively with different supervised classification methods. Experimental heart arrthymia data has been obtained from MIT-BIH arrthymia database. Total of 1200 training and 1200 test samples have been chosen equally for 6 classes from the database. For the purpose of increasing the accuracy with chosen datasets, mixed noises from different sources in the ECG signals are removed with signal processing methods. Support vector machines (SVM) and statistical neural networks (RBF, PNN and GRNN) are utilized for classification purpose. In the experimental results it has been observed that the best accuracy is accomplished by RBF kernel SVM, trained with any of the two mentioned feature extraction methods.