Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Inductive Learning Algorithms for Complex Systems Modeling
Inductive Learning Algorithms for Complex Systems Modeling
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Radial Basis Functions
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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Long term Holter monitoring is widely applied to patients with heart diseases. Many of those diseases are not constantly present in the ECG signal but occurs from time to time. To detect these infrequent problems the Holter long time ECG recording is recorded and analysed. There are many methods for automatic detection of irregularities in the ECG signal. In this paper we will comapare the Support Vector Machine (SVM), J48 decision tree (J48), RBF artificial neural network (RBF), Simple logistic function and our novel GAME neural network for detection of the Premature Ventricular Contractions. We will compare and discuss classification performance of mentioned methods. There are also very many features which describes the ECG signal therefore we will try to identify features important for correct classification and examine how the accuracy is affected with only selected features in training set.