  Multi-lead electrocardiogram (ECG) provides clinical information of
heartbeats from several fixed viewpoints determined by the lead positioning.
However, it is often not satisfactory to visualize ECG signals in these fixed
and limited views, as some clinically useful information is represented only
from a few specific ECG viewpoints. For the first time, we propose a new
concept, Electrocardio Panorama, which allows visualizing ECG signals from any
queried viewpoints. To build Electrocardio Panorama, we assume that an
underlying electrocardio field exists, representing locations, magnitudes, and
directions of ECG signals. We present a Neural electrocardio field Network
(Nef-Net), which first predicts the electrocardio field representation by using
a sparse set of one or few input ECG views and then synthesizes Electrocardio
Panorama based on the predicted representations. Specially, to better
disentangle electrocardio field information from viewpoint biases, a new
Angular Encoding is proposed to process viewpoint angles. Also, we propose a
self-supervised learning approach called Standin Learning, which helps model
the electrocardio field without direct supervision. Further, with very few
modifications, Nef-Net can also synthesize ECG signals from scratch.
Experiments verify that our Nef-Net performs well on Electrocardio Panorama
synthesis, and outperforms the previous work on the auxiliary tasks (ECG view
transformation and ECG synthesis from scratch). The codes and the division
labels of cardiac cycles and ECG deflections on Tianchi ECG and PTB datasets
are available at https://github.com/WhatAShot/Electrocardio-Panorama.
