A feature enrichment-based CNN classifier improved the F1 score for supraventricular ectopic beat detection from 0.75 to 0.82 compared to state-of-the-art algorithms on the MIT-BIH database.
Does a Feature Enrichment Based Convolutional Neural Network (FE-CNN) improve the accuracy of heartbeat classification from ECG signals compared to state-of-the-art algorithms?
A novel feature enrichment-based CNN classifier improves the accuracy of detecting supraventricular and ventricular ectopic beats from ECG signals without requiring hand-crafted features.
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.
Xie et al. (Tue,) conducted a other in Arrhythmia. Feature Enrichment Based Convolutional Neural Network (FE-CNN) vs. State-of-the-art algorithms including other CNN based methods was evaluated on Detection of supraventricular ectopic (S) and ventricular ectopic (V) beats (Sensitivity, Positive predictive rate, F1 score). A feature enrichment-based CNN classifier improved the F1 score for supraventricular ectopic beat detection from 0.75 to 0.82 compared to state-of-the-art algorithms on the MIT-BIH database.