Over the last few decades, the application of different mathematical and statistical models has received great attention to interpret and classify pathological cardiac events. In the cardiac disease therapy, the analysis of cardiac health condition depends on satisfactory discrimination of electrocardiogram (ECG) signals [1]. Performance of heartbeat classification is of great importance for early detection of cardiac abnormality. The aim of this paper is to present a comparative study of three classification techniques based on a couple of characteristic heartbeat features. Study was conducted using ECG data taken from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) arrhythmia database to differentiate between normal and abnormal beats [2]. In this paper, three different classification techniques have been compared: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and probabilistic neural network (PNN). In Ref. [3], ECG signal using linear discriminant classifier...

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