A novel deep learning package for electrocardiography research

计算机科学 深度学习 人工智能 人工神经网络 机器学习 预处理器 可扩展性 信号处理 数据预处理 数据挖掘 数字信号处理 计算机硬件 数据库
作者
Hao Wen,Jingsu Kang
出处
期刊:Physiological Measurement [IOP Publishing]
卷期号:43 (11): 115006-115006 被引量:2
标识
DOI:10.1088/1361-6579/ac9451
摘要

Objective. In recent years, deep learning has blossomed in the field of electrocardiography (ECG) processing, outperforming traditional signal processing methods in a number of typical tasks; for example, classification, QRS detection and 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.Approach. In this paper, we propose a deep learning package, namedtorch_ecg, which assembles a large number of neural networks, from existing and novel literature, for various ECG processing tasks. The models are designed to be able to be automatically built from configuration files that contain a large set of configurable hyperparameters, making it convenient to scale the networks and perform neural architecture searching.torch_ecghas well-organized data processing modules, which contain utilities for data downloading, visualization, preprocessing and augmentation. To make the whole system more user-friendly, a series of helper modules are implemented, including model trainers, metric computation and loggers.Main results.torch_ecgestablishes a convenient and modular way for automatic building and flexible scaling of 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. In addition,torch_ecgprovides benchmark studies using the latest databases, illustrating the principles and pipelines for solving ECG processing tasks and reproducing results from the literature.Significance.torch_ecgoffers the ECG research community a powerful tool for meeting the growing demand for the application of deep learning techniques. The code is available athttps://github.com/DeepPSP/torch_ecg.
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