PCA representation of ECG signal as a useful tool for detection of premature ventricular beats in 3-channel Holter recording by neural network and support vector machine classifier

Abstract

In the paper classification method of compressed ECG signal was presented. Classification of single heartbeats was performed by neural networks and support vector machine. Parameterization of ECG signal was realized by principal component analysis (PCA). For every heartbeat only two descriptors have been used. The results of real Holter signal were presented in tables and as plots in planespherical coordinates. The efficiency of classification is near to 99%. © Springer-Verlag Berlin Heidelberg 2004.