论文标题
研究心电图信号处理的深度学习基准测试
Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing
论文作者
论文摘要
近年来，深度学习见证了其在心电图（ECG）处理领域的开花，在各种任务中表现优于传统的信号处理方法，例如分类，QRS检测，波浪描述。尽管文献中已经提出了许多神经体系结构，但缺乏系统的研究和开源库来进行ECG深度学习。 在本文中，我们提出了一个深度学习框架，称为\ texttt {Torch \ _ecg}，该框架用于各种ECG处理任务，从文学和新颖的过程中收集了许多神经网络。它为网络的自动构建和灵活缩放建立了一种方便，模块化的方式，以及组织预处理程序和增强技术的整洁而统一的方法，以准备模型的输入数据。此外，\ texttt {Torch \ _ecg}使用最新数据库提供基准研究，说明了解决ECG处理任务的原理和管道，并从文献中重现了结果。 \ texttt {Torch \ _ecg}为ECG研究社区提供了一种强大的工具，满足了对深度学习技术的不断增长的需求。
In recent years, deep learning has witnessed its blossom in the field of Electrocardiography (ECG) processing, outperforming traditional signal processing methods in various tasks, for example, classification, QRS detection, wave delineation. Although many neural architectures have been proposed in the literature, there is a lack of systematic studies and open-source libraries for ECG deep learning. In this paper, we propose a deep learning framework, named \texttt{torch\_ecg}, which gathers a large number of neural networks, both from literature and novel, for various ECG processing tasks. It establishes a convenient and modular way for automatic building and flexible scaling of the networks, as well as a neat and uniform way of organizing the preprocessing procedures and augmentation techniques for preparing the input data for the models. Besides, \texttt{torch\_ecg} provides benchmark studies using the latest databases, illustrating the principles and pipelines for solving ECG processing tasks and reproducing results from the literature. \texttt{torch\_ecg} offers the ECG research community a powerful tool meeting the growing demand for the application of deep learning techniques.
