Classification of Cardiac Abnormalities Using Reduced Features of Heart Rate Variability Signal

This paper presents an effective arrhythmia classification algorithm using the heart rate variability (HRV) signal. The proposed method is based on the Generalized Discriminant Analysis (GDA) feature reduction technique and the Multilayer Perceptron (MLP) neural network classifier. At first, nine linear and nonlinear features are extracted from the HRV signals and then these features are reduced to only three by GDA. Finally, the MLP neural network is used to classify the HRV signals. The proposed arrhythmia classification method is applied to input HRV signals, obtained from the MIT-BIH databases. Here, four types of the most life threatening cardiac arrhythmias including left bundle branch block, fist degree heart block, Supraventricular tachyarrhythmia and ventricular trigeminy can be discriminated by MLP and reduced features with the accuracy of 100%.