  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.
