ECG arrhythmia classification using a probabilistic neural network with a feature reduction method

This paper presents an effective electrocardiogram (ECG) arrhythmia classification scheme consisting of a feature reduction method combining principal component analysis (PCA) with linear discriminant analysis (LDA), and a probabilistic neural network (PNN) classifier to discriminate eight different types of arrhythmia from ECG beats. Each ECG beat sample composed of 200 sampling points at a 360 Hz sampling rate around an R peak is extracted from ECG signals. The feature reduction method is employed to find important features from ECG beats, and to improve the classification accuracy of the classifier. With the selected features, the PNN is then trained to serve as a classifier for discriminating eight different types of ECG beats. The average classification accuracy of the proposed scheme is 99.71%. Our experimental results have successfully validated that the integration of the PNN classifier with the proposed feature reduction method can achieve satisfactory classification accuracy.