Classification of arrhythmias using statistical features in the wavelet transform domain

Computer assisted recognition and classification of ECG into different pathophysiological disease categories is critical for diagnosis of cardiac abnormalities. Evaluation and prediction of life threatening ventricular arrhythmias greatly depend on Premature Ventricular Contraction (PVC) beats. Many studies have revealed that PVCs when associated with myocardial infarction can be linked to mortality. Hence their immediate detection and treatment is crucial for patients with heart diseases. This work focus on improving the automatic diagnosis of PVC arrhythmia from ECG signals. Out of the different methods for ECG analysis, this work adopts sectional analysis of ECG and suitable statistical features in the wavelet transform domain were calculated. These features were utilized to train Support Vector Machines (SVM) classifier and to classify the ECG as normal or with PVC. Advancement of this work is based on an appropriate choice of minimal statistical features which gives better classification in least time.