Feature Enrichment Based Convolutional Neural Network for Heartbeat Classification from Electrocardiogram

Correct heartbeat classification from electrocardiogram (ECG) signals is fundamental to the diagnosis of arrhythmia. The recent advancement in deep convolutional neural network (CNN) has renewed the interest in applying deep learning techniques to improve the accuracy of heartbeat classification. So far, the results are not very exciting. Most of the existing methods are based on ECG morphological information, which makes deep learning difficult to extract discriminative features for classification. Towards an opposite direction of feature extraction or selection, this paper proceeds along a recent proposed direction named feature enrichment (FE). To exploit the advantage of deep learning, we develop a FE-CNN classifier by enriching the ECG signals into time-frequency images by discrete short-time Fourier transform and then using the images as the input to CNN. Experiments on MIT-BIH arrhythmia database show FE-CNN obtains sensitivity ( $Sen$ ) of 75.6%, positive predictive rate ( $Ppr$ ) of 90.1%, and F1 score of 0.82 for the detection of supraventricular ectopic (S) beats. $Sen$ , $Ppr$ , and F1 score are 92.8%, 94.5%, and 0.94, respectively, for ventricular ectopic (V) beat detection. The result demonstrates our method outperforms state-of-the-art algorithms including other CNN based methods, without any hand-crafted features, especially F1 score for S beat detection from 0.75 to 0.82. This FE-CNN classifier is simple, effective, and easy to be applied to other types of vital signs.