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A deep biometric recognition and diagnosis network with residual learning for arrhythmia screening using electrocardiogram recordings
2020
IEEE Access
Arrhythmia is one of the most persistent chronic heart diseases in the elderly and is associated with high morbidity and mortality such as stroke, cardiac failure, and coronary artery diseases. It is significant for patients with arrhythmias to automatically detect and classify arrhythmia heartbeats using electrocardiogram (ECG) signals. In this paper, we develop three robust deep convolutional neural network (DCNN) models, including a plain-CNN network and two MSF(multi-scale fusion)-CNN
doi:10.1109/access.2020.3016938
fatcat:jkuqswdswzg7tc7lh5o76hu7lu