An Efficient Technique for Classification of Electrocardiogram Signals

This work describes a Radial Basis Function (RBF) neural network method used to analyze ECG signals for diagnosing cardiac arrhythmias effectively. The proposed method can accurately classify and differentiate normal (Normal) and abnormal heartbeats. Abnormal heartbeats include left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature ventricular contractions (PVC). This paper proposes a three stage, preprocessing, feature extraction and classification method for the detection of ECG beat types. In the first stage, ECG beats is normalized to a mean of zero and standard deviation of unity. Feature extraction module extracts wavelet approximate coefficients of ECG signals in conjunction with three timing interval features. Then a number of radial basis function (RBF) neural networks with different value of spread parameter are designed. We compared the classification ability of five different classes of ECG signals that were achieved over eight files from the MIT/BIH arrhythmia database.