Automatic concurrent arrhythmia classification using deep residual neural networks
This paper addresses the PhysioNetlComputing in Cardiology Challenge 2020. The
challenge presents a problem to classify 26 types of arrhythmias and normal sinus rhythm
using 12-lead electrocardiogram data. We were able to successfully perform the
classification task using an eight layer deep residual neural network (ResNet). The skip
connections present in the ResNet allowed the model to train faster and produce better
challenge score. We also investigated sixteen other models that included convolution and …
challenge presents a problem to classify 26 types of arrhythmias and normal sinus rhythm
using 12-lead electrocardiogram data. We were able to successfully perform the
classification task using an eight layer deep residual neural network (ResNet). The skip
connections present in the ResNet allowed the model to train faster and produce better
challenge score. We also investigated sixteen other models that included convolution and …
以上显示的是最相近的搜索结果。 查看全部搜索结果