Deep Learning-Based Arrhythmia Detection in Electrocardiograph

Abstract

This study aimed to explore the application of electrocardiograph (ECG) in the diagnosis of arrhythmia based on the deep convolutional neural network (DCNN). ECG was classified and recognized with the DCNN. The specificity (Spe), sensitivity (Sen), accuracy (Acc), and area under curve (AUC) of the DCNN were evaluated in the Chinese Cardiovascular Disease Database (CCDD) and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, respectively. The results showed that in the CCDD, the original model tested by the small sample set had an accuracy (Acc) of 82.78% and AUC of 0.882, while the Acc and AUC of the translated model were 85.69% and 0.893, respectively, so the difference was notable (P < 0.05); the Acc of the original model and the translated model was 80.12% and 82.63%, respectively, in the large sample set, so the difference was obvious (P < 0.05). In the MIT-BIH database, the Acc of normal (N) heart beat (HB) (99.38%) was higher than that of the atrial premature beat (APB) (87.45%) (P < 0.05). In a word, applying the DCNN could improve the Acc of ECG for classification and recognition, so it could be well applied to ECG signal classification.