An optimized deep neural network can accurately classify premature ventricular contraction beats from ECG data with high sensitivity.
A deep neural networks is proposed for the classification of premature ventricular contraction (PVC) beat, which is an irregular heartbeat initiated by Purkinje fibers rather than by sinoatrial node. Several machine learning approaches were proposed for the detection of PVC beats although they resulted in either achieving low accuracy of classification or using limited portion of data from existing electrocardiography (ECG) databases. In this paper, we propose an optimized deep neural networks for PVC beat classification. Our method is evaluated on TensorFlow, which is an open source machine learning platform initially developed by Google. Our method achieved overall 99.41% accuracy and a sensitivity of 96.08% with total 80,836 ECG beats including normal and PVC from the MIT-BIH Arrhythmia Database.
Jun et al. (2016) studied this question.