Kernel-based feature extraction for patient-adaptive ECG beat classification

The increasing cardiac diseases of people in recent years demand an early detection of heart diseases using electrocardiogram (ECG) signal processing techniques. In this work we present a semi automatic scheme to discriminate patient-specific ECG beats by using a kernel based feature extraction technique called kernel canonical correlation analysis (KCCA). The heartbeat classification scheme uses a fixed global training set along with a local patient specific training set to produce a patient adapted training set. The performance of the KCCA features is verified on MIT-BIH arrhythmia database by using three classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM) and vector valued regularized kernel function approximation (VVRKFA), to classify four different type of beats, normal (N), supra-ventricular (S), ventricular (V) and fusion of ventricular and normal (F). Experimental results show that KCCA features are more effective on normal patients than that of arrhythmia patients.