Higher order statistics for automated classification of ECG beats

This work describes a Support Vector Machine (SVM) 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 two stage, feature extraction and classification method for the detection of ECG beat types. Feature extraction module extracts higher order statistics of ECG signals in conjunction with three timing interval features. Then a number of support vector machine (SVM) classifiers with different value parameters are designed. These parameters are: Gaussian radial basis function (GRBF) kernel parameter and C penalty parameter of SVM classifier. We compared the classification ability of five different classes of ECG signals that were achieved over eight files from the MIT/BIH arrhythmia database.