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Feature Vector Selection for Automatic Classification of ECG Arrhythmias

Ch.Venkanna, B. Raja Ganapathi

The processing of ECG signal plays a significant role in diagnosing most of the cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. The feature extraction scheme determines the amplitudes and intervals in the ECG signal for subsequent analysis. The shape of ECG waveform can reveal the current state of functionality of the heart. For all these observation of anomalies and selection of feature vector is important. Based on the selected feature vector classification is done. The classification is done by using RBF neural network for the analysis and evaluation of Feature Vector Selection of ECG Arrhythmias.