论文标题
心律不齐的深度学习分类器
A deep-learning classifier for cardiac arrhythmias
论文作者
论文摘要
我们报告了一种根据13个类别（包括心律不齐）的13个类别对心脏跳动进行分类的方法。该方法定位QRS峰复合体来定义每个心脏跳动，并使用神经网络来推断每个心跳类别的模式特征。表现最佳的神经网络包含六个一维卷积层和四个密集的层，其中内核大小是问题的特征量表的倍数，从而导致计算快速且具有物理动机的神经网络。对于相同数量的心跳班，我们的方法通过与以前发表的方法相比，通过更小的神经网络产生更好的结果，这使得我们在本网络解决方案中部署的方法具有竞争力。
We report on a method that classifies heart beats according to a set of 13 classes, including cardiac arrhythmias. The method localises the QRS peak complex to define each heart beat and uses a neural network to infer the patterns characteristic of each heart beat class. The best performing neural network contains six one-dimensional convolutional layers and four dense layers, with the kernel sizes being multiples of the characteristic scale of the problem, thus resulting a computationally fast and physically motivated neural network. For the same number of heart beat classes, our method yields better results with a considerably smaller neural network than previously published methods, which renders our method competitive for deployment in an internet-of-things solution.
