论文标题
心电图细分的深度学习
Deep Learning for ECG Segmentation
论文作者
论文摘要
我们提出了一种使用UNET样的全趋化神经网络进行心电图（ECG）分割的算法。该算法作为输入接收任意采样率ECG信号，并给出了P和T波和QRS复合物作为输出的启用和偏移列表。我们的分割方法在速度方面不同于其他参数，少量参数和良好的概括：它适应不同的采样率，并且已推广到各种类型的ECG监视器。就质量而言，所提出的方法优于其他最先进的细分方法。尤其是，用于检测P和T波的吞吐量和偏移以及QRS复合物的F1测量分别为97.8％，99.5％和99.9％。
We propose an algorithm for electrocardiogram (ECG) segmentation using a UNet-like full-convolutional neural network. The algorithm receives an arbitrary sampling rate ECG signal as an input, and gives a list of onsets and offsets of P and T waves and QRS complexes as output. Our method of segmentation differs from others in speed, a small number of parameters and a good generalization: it is adaptive to different sampling rates and it is generalized to various types of ECG monitors. The proposed approach is superior to other state-of-the-art segmentation methods in terms of quality. In particular, F1-measures for detection of onsets and offsets of P and T waves and for QRS-complexes are at least 97.8%, 99.5%, and 99.9%, respectively.
